Meet CodeAct Agent: Your versatile open-source LLM that not only chats but also writes and executes Python code for you.

Disclaimer: AI is an area of active research with known problems such as biased generation and misinformation. Do not use this application for high-stakes decisions or advice.

Here is a dataset about auto MPG: "https://huggingface.co/datasets/scikit-learn/auto-mpg/raw/main/auto-mpg.csv".
You should download and load the dataset from the URL, take a look at it, check for missing value, do a train-test split, and train a regression model predicting its MPG.
You should use everything except "mpg" and "car name" as the input features.

To accomplish this task, I will use the pandas library to download and load the dataset, check for missing values, and perform a train-test split. Then, I will use the scikit-learn library to train a regression model.

EXECUTE

```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# Download and load the dataset
df = pd.read_csv("https://huggingface.co/datasets/scikit-learn/auto-mpg/raw/main/auto-mpg.csv")
# Check for missing values
print(df.isnull().sum())
# Remove rows with missing values
df = df.dropna()
# Perform train-test split
X = df.drop(columns=["mpg", "car name"])
y = df["mpg"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Predict MPG for test data
y_pred = model.predict(X_test)
# Evaluate the model
print("Mean squared error: ", np.mean((y_test - y_pred) ** 2))
print("R^2 score: ", r2_score(y_test, y_pred))
```

STDOUT/STDERR

```
mpg 0
cylinders 0
displacement 0
horsepower 0
weight 0
acceleration 0
model year 0
origin 0
car name 0
dtype: int64
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_12/1372561217.py in ?()
18 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
19
20 # Train a regression model
21 model = LinearRegression()
---> 22 model.fit(X_train, y_train)
23
24 # Predict MPG for test data
25 y_pred = model.predict(X_test)
/opt/conda/lib/python3.11/site-packages/sklearn/base.py in ?(estimator, *args, **kwargs)
1148 skip_parameter_validation=(
1149 prefer_skip_nested_validation or global_skip_validation
1150 )
1151 ):
-> 1152 return fit_method(estimator, *args, **
... [Output truncated due to length]...
) from complex_warning
/opt/conda/lib/python3.11/site-packages/sklearn/utils/_array_api.py in ?(array, dtype, order, copy, xp)
376 # Use NumPy API to support order
377 if copy is True:
378 array = numpy.array(array, order=order, dtype=dtype)
379 else:
--> 380 array = numpy.asarray(array, order=order, dtype=dtype)
381
382 # At this point array is a NumPy ndarray. We convert it to an array
383 # container that is consistent with the input's namespace.
/opt/conda/lib/python3.11/site-packages/pandas/core/generic.py in ?(self, dtype)
2082 def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
2083 values = self._values
-> 2084 arr = np.asarray(values, dtype=dtype)
2085 if (
2086 astype_is_view(values.dtype, arr.dtype)
2087 and using_copy_on_write()
ValueError: could not convert string to float: '?'
```

It seems that there is a data type issue in the dataset. The '?' character is present in the data, which is causing the error when trying to convert the data to a float. To fix this, I will remove the rows containing the '?' character.

EXECUTE

```
# Remove rows containing '?'
df = df.replace('?', np.nan)
df = df.dropna()
X = df.drop(columns=["mpg", "car name"])
y = df["mpg"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Mean squared error: ", np.mean((y_test - y_pred) ** 2))
print("R^2 score: ", r2_score(y_test, y_pred))
```

STDOUT/STDERR

```
Mean squared error: 10.710864418838367
R^2 score: 0.7901500386760351
```

The mean squared error is 10.711 and the R^2 score is 0.790, which indicates that the model has a good fit with the data. The mean squared error is a measure of the average squared difference between the predicted and actual values, and a lower value indicates a better fit. The R^2 score is a measure of the proportion of the variance in the actual values that is explained by the model, and a value closer to 1 indicates a better fit.

In summary, the model has performed well in predicting the MPG values of the cars in the test dataset.

Certainly! Here are the mean squared error (MSE) and R^2 scores for the training set:

EXECUTE

```
from sklearn.metrics import mean_squared_error
# Calculate MSE for training set
mse_train = mean_squared_error(y_train, model.predict(X_train))
# Calculate R^2 score for training set
r2_train = r2_score(y_train, model.predict(X_train))
print("Mean squared error (training set):", mse_train)
print("R^2 score (training set):", r2_train)
```

STDOUT/STDERR

```
Mean squared error (training set): 10.979255842373478
R^2 score (training set): 0.826001578671067
```

The mean squared error for the training set is 10.979 and the R^2 score is 0.826, which indicates that the model has a good fit with the training data. The mean squared error is a measure of the average squared difference between the predicted and actual values, and a lower value indicates a better fit. The R^2 score is a measure of the proportion of the variance in the actual values that is explained by the model, and a value closer to 1 indicates a better fit.

In summary, the model has performed well in predicting the MPG values of the cars in the training dataset.

Certainly! Here is a visualization of the regression coefficients for each input feature:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
```

The regression coefficients are plotted as a bar chart, with each input feature on the x-axis and the corresponding coefficient on the y-axis. The coefficients are positive or negative numbers that represent the change in the predicted MPG for a unit change in the corresponding input feature. A positive coefficient indicates that the input feature has a positive effect on the predicted MPG, while a negative coefficient indicates that the input feature has a negative effect on the predicted MPG.

In summary, the regression coefficients provide insight into which input features have the greatest impact on the predicted MPG values.

Certainly! Here is the updated bar chart with the x-ticks rotated by 45 degrees for better readability:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef, tick_labels=False)
plt.xticks(feature_names, feature_names, rotation=45, ha='right', fontsize=12)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[1], line 10
7 feature_names = model.feature_names_in_
9 # Plot regression coefficients
---> 10 plt.bar(feature_names, coef, tick_labels=False)
11 plt.xticks(feature_names, feature_names, rotation=45, ha='right', fontsize=12)
12 plt.xlabel('Input Feature')
File /opt/conda/lib/python3.11/site-packages/matplotlib/pyplot.py:2742, in bar(x, height, width, bottom, align, data, **kwargs)
2731 @_copy_docstring_and_deprecators(Axes.bar)
2732 def bar(
2733 x: float | ArrayLike,
(...)
2740 **kwargs,
2741 ) -> BarContainer:
-> 2742 return gca().bar(
2743 x,
2744 height,
2745 width=width,
2746 bottom=bottom,
2747 align=align,
2748 **({"data": data
... [Output truncated due to length]...
on3.11/site-packages/matplotlib/artist.py:1219, in Artist._internal_update(self, kwargs)
1212 def _internal_update(self, kwargs):
1213 """
1214 Update artist properties without prenormalizing them, but generating
1215 errors as if calling `set`.
1216
1217 The lack of prenormalization is to maintain backcompatibility.
1218 """
-> 1219 return self._update_props(
1220 kwargs, "{cls.__name__}.set() got an unexpected keyword argument "
1221 "{prop_name!r}")
File /opt/conda/lib/python3.11/site-packages/matplotlib/artist.py:1193, in Artist._update_props(self, props, errfmt)
1191 func = getattr(self, f"set_{k}", None)
1192 if not callable(func):
-> 1193 raise AttributeError(
1194 errfmt.format(cls=type(self), prop_name=k))
1195 ret.append(func(v))
1196 if ret:
AttributeError: Rectangle.set() got an unexpected keyword argument 'tick_labels'
```

It seems that the `tick_labels`

argument is not a valid argument for the `bar()`

function in Matplotlib. Instead, we can use the `tick_params()`

function to rotate the x-ticks. Here is the updated bar chart with the x-ticks rotated by 45 degrees for better readability:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef)
plt.tick_params(axis='x', labelrotation=45, ha='right', fontsize=12)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 11
9 # Plot regression coefficients
10 plt.bar(feature_names, coef)
---> 11 plt.tick_params(axis='x', labelrotation=45, ha='right', fontsize=12)
12 plt.xlabel('Input Feature')
13 plt.ylabel('Regression Coefficient')
File /opt/conda/lib/python3.11/site-packages/matplotlib/pyplot.py:3948, in tick_params(axis, **kwargs)
3946 @_copy_docstring_and_deprecators(Axes.tick_params)
3947 def tick_params(axis: Literal["both", "x", "y"] = "both", **kwargs) -> None:
-> 3948 gca().tick_params(axis=axis, **kwargs)
File /opt/conda/lib/python3.11/site-packages/matplotlib/axes/_base.py:3389, in _AxesBase.tick_params(self, axis, **kwargs)
3387 xkw.pop('labelleft', None)
3388 xkw.pop('labelright', None)
-> 3389 s
... [Output truncated due to length]...
rection', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']
```

It seems that the `tick_params()`

function is not working as expected. Instead, we can use the `set_ticks()`

function to set the x-ticks and rotate them by 45 degrees for better readability. Here is the updated bar chart with the x-ticks rotated by 45 degrees:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef)
plt.set_ticks(feature_names, feature_names, rotation=45, ha='right', fontsize=12)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[1], line 11
9 # Plot regression coefficients
10 plt.bar(feature_names, coef)
---> 11 plt.set_ticks(feature_names, feature_names, rotation=45, ha='right', fontsize=12)
12 plt.xlabel('Input Feature')
13 plt.ylabel('Regression Coefficient')
AttributeError: module 'matplotlib.pyplot' has no attribute 'set_ticks'
```

I apologize for the confusion. It seems that the `set_ticks()`

function is not a valid function in Matplotlib. Instead, we can use the `set_xticks()`

function to set the x-ticks and rotate them by 45 degrees for better readability. Here is the updated bar chart with the x-ticks rotated by 45 degrees:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef)
plt.set_xticks(feature_names, rotation=45, ha='right', fontsize=12)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[1], line 11
9 # Plot regression coefficients
10 plt.bar(feature_names, coef)
---> 11 plt.set_xticks(feature_names, rotation=45, ha='right', fontsize=12)
12 plt.xlabel('Input Feature')
13 plt.ylabel('Regression Coefficient')
AttributeError: module 'matplotlib.pyplot' has no attribute 'set_xticks'
```

I apologize for the confusion. It seems that the `set_xticks()`

function is not a valid function in Matplotlib. Instead, we can use the `xticks()`

function to set the x-ticks and rotate them by 45 degrees for better readability. Here is the updated bar chart with the x-ticks rotated by 45 degrees:

EXECUTE

```
import matplotlib.pyplot as plt
# Get regression coefficients
coef = model.coef_
# Get names of input features
feature_names = model.feature_names_in_
# Plot regression coefficients
plt.bar(feature_names, coef)
plt.xticks(feature_names, feature_names, rotation=45, ha='right', fontsize=12)
plt.xlabel('Input Feature')
plt.ylabel('Regression Coefficient')
plt.title('Regression Coefficients')
# Display the plot
plt.show()
```

STDOUT/STDERR

```
<Figure size 640x480 with 1 Axes>
[An image is already displayed to the user.]
```

I have reached the maximum number of executions specified by the administrator for this turn (=5). Can you assist me or ask me another question? You can also ask me to continue.

Model: CodeActAgent-Mistral-7b-v0.1 Â·

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or false.