# π Exercise M1.02ΒΆ

The goal of this exercise is to fit a similar model as in the previous
notebook to get familiar with manipulating scikit-learn objects and in
particular the `.fit/.predict/.score`

API.

Letβs load the adult census dataset with only numerical variables

```
import pandas as pd
adult_census = pd.read_csv("../datasets/adult-census-numeric.csv")
data = adult_census.drop(columns="class")
target = adult_census["class"]
```

In the previous notebook we used `model = KNeighborsClassifier()`

. All
scikit-learn models can be created without arguments, which means that you
donβt need to understand the details of the model to use it in scikit-learn.

One of the `KNeighborsClassifier`

parameters is `n_neighbors`

. It controls
the number of neighbors we are going to use to make a prediction for a new
data point.

What is the default value of the `n_neighbors`

parameter? Hint: Look at the
help inside your notebook `KNeighborsClassifier?`

or on the scikit-learn
website

Create a `KNeighborsClassifier`

model with `n_neighbors=50`

```
# Write your code here.
```

Fit this model on the data and target loaded above

```
# Write your code here.
```

Use your model to make predictions on the first 10 data points inside the data. Do they match the actual target values?

```
# Write your code here.
```

Compute the accuracy on the training data.

```
# Write your code here.
```

Now load the test data from `"../datasets/adult-census-numeric-test.csv"`

and
compute the accuracy on the test data.

```
# Write your code here.
```