We need to clean the data before we can use it to train a model. Here are some things we need to do:
- Remove any rows with missing values.
- Convert the categorical columns to numerical values.
To remove any rows with missing values, we can use the dropna() method:
df = df.dropna()
To convert the categorical columns to numerical values, we can use the OneHotEncoder() class:
encoder = OneHotEncoder()
# Convert the categorical columns to numerical values
df_encoded = encoder.fit_transform(df[['origin']])
This will create a new DataFrame called df_encoded that contains the numerical values for the categorical columns.
The first step in cleaning the data is to identify any missing values. We can do this using the isna() function. Any rows with missing values will be returned as a boolean value. We can then use the dropna() function to remove these rows from the dataset.
import pandas as pd
cars = pd.read_csv('cars.csv')
# Identify any missing values
missing_values = cars.isna()
# Remove any rows with missing values
cars = cars.dropna()
Next, we need to check for any outliers. Outliers are data points that are significantly different from the rest of the data. We can identify outliers using the boxplot() function.
# Plot a boxplot of the horsepower column
cars['horsepower'].plot(kind='box')
The boxplot shows that there are a few outliers in the horsepower column. We can remove these outliers using the drop() function.
# Remove any outliers from the horsepower column
cars = cars[cars['horsepower'] <= 300]
Finally, we need to normalize the data. Normalization is a process of scaling the data so that it has a mean of 0 and a standard deviation of 1. This is important because it helps the model to learn more effectively.
# Normalize the data
cars = cars.normalize()
Once the data is loaded, we need to clean it. This includes removing any rows with missing values, and converting any categorical data to numerical data.
To remove rows with missing values, we can use the dropna() method. To convert categorical data to numerical data, we can use the get_dummies() method.
The following code shows how to remove rows with missing values and convert categorical data to numerical data:
df = df.dropna()
df = pd.get_dummies(df, columns=['origin'])
