Project 2: Predicting House Prices
Feature Engineering
By now, you've got your data looking all neat and tidy, but guess what? We can make it even better. How, you ask? Through the magic of Feature Engineering!
Feature engineering is essentially the art of enhancing your dataset with new features that could help your model make better predictions. It's like adding spices to a dish to make it even more delicious. Let's walk through this important step in our house prices prediction project!
Creating Polynomial Features
One way to enhance our dataset is by creating polynomial features. This involves creating new features that are powers of existing ones. For instance, if we have a feature that represents the number of bedrooms, we could create another feature that's the square of the number of bedrooms.
# Creating a new feature 'Bedrooms_Squared'
df['Bedrooms_Squared'] = df['Bedrooms'] ** 2Interaction Terms
You can also create interaction terms between two different variables. Let's say our dataset has 'Square Footage' and 'Number of Bedrooms'; the interaction would be 'Square Footage per Bedroom.'
# Creating a new feature 'Square_Footage_per_Bedroom'
df['Square_Footage_per_Bedroom'] = df['Square_Footage'] / df['Bedrooms']Categorical Feature Engineering
If you have categorical features that are not capturing the nuances in your data, you can add more granularity. For example, instead of a broad category like 'Neighborhood', you could have 'Close_to_School_in_Neighborhood'.
# Engineering a new feature based on existing categorical features
df['Close_to_School_in_Neighborhood'] = df['Neighborhood'] + "_" + df['Close_to_School'].astype(str)Temporal Features
If your dataset contains date variables, you can extract valuable information from them. For example, if you have the 'Date of Sale', you could extract the 'Month of Sale' or 'Quarter of Sale'.
# Converting 'Date_of_Sale' to datetime format
df['Date_of_Sale'] = pd.to_datetime(df['Date_of_Sale'])
# Extracting the quarter
df['Quarter_of_Sale'] = df['Date_of_Sale'].dt.quarterFeature Transformation
Sometimes, a little mathematical tweak can go a long way. Log transformations are quite useful for dealing with skewed data.
import numpy as np
# Applying log transformation
df['Log_Square_Footage'] = np.log(df['Square_Footage'])Alrighty then! You've successfully engineered some incredibly useful features for your dataset. You might think of this as an optional step, but trust us, good feature engineering can be the difference between a mediocre model and a fantastic one.
In the next section, we'll venture into the thrilling lands of machine learning models, where all your hard work so far will start to pay off. Can't wait to see you there!
Feature Engineering
By now, you've got your data looking all neat and tidy, but guess what? We can make it even better. How, you ask? Through the magic of Feature Engineering!
Feature engineering is essentially the art of enhancing your dataset with new features that could help your model make better predictions. It's like adding spices to a dish to make it even more delicious. Let's walk through this important step in our house prices prediction project!
Creating Polynomial Features
One way to enhance our dataset is by creating polynomial features. This involves creating new features that are powers of existing ones. For instance, if we have a feature that represents the number of bedrooms, we could create another feature that's the square of the number of bedrooms.
# Creating a new feature 'Bedrooms_Squared'
df['Bedrooms_Squared'] = df['Bedrooms'] ** 2Interaction Terms
You can also create interaction terms between two different variables. Let's say our dataset has 'Square Footage' and 'Number of Bedrooms'; the interaction would be 'Square Footage per Bedroom.'
# Creating a new feature 'Square_Footage_per_Bedroom'
df['Square_Footage_per_Bedroom'] = df['Square_Footage'] / df['Bedrooms']Categorical Feature Engineering
If you have categorical features that are not capturing the nuances in your data, you can add more granularity. For example, instead of a broad category like 'Neighborhood', you could have 'Close_to_School_in_Neighborhood'.
# Engineering a new feature based on existing categorical features
df['Close_to_School_in_Neighborhood'] = df['Neighborhood'] + "_" + df['Close_to_School'].astype(str)Temporal Features
If your dataset contains date variables, you can extract valuable information from them. For example, if you have the 'Date of Sale', you could extract the 'Month of Sale' or 'Quarter of Sale'.
# Converting 'Date_of_Sale' to datetime format
df['Date_of_Sale'] = pd.to_datetime(df['Date_of_Sale'])
# Extracting the quarter
df['Quarter_of_Sale'] = df['Date_of_Sale'].dt.quarterFeature Transformation
Sometimes, a little mathematical tweak can go a long way. Log transformations are quite useful for dealing with skewed data.
import numpy as np
# Applying log transformation
df['Log_Square_Footage'] = np.log(df['Square_Footage'])Alrighty then! You've successfully engineered some incredibly useful features for your dataset. You might think of this as an optional step, but trust us, good feature engineering can be the difference between a mediocre model and a fantastic one.
In the next section, we'll venture into the thrilling lands of machine learning models, where all your hard work so far will start to pay off. Can't wait to see you there!
Feature Engineering
By now, you've got your data looking all neat and tidy, but guess what? We can make it even better. How, you ask? Through the magic of Feature Engineering!
Feature engineering is essentially the art of enhancing your dataset with new features that could help your model make better predictions. It's like adding spices to a dish to make it even more delicious. Let's walk through this important step in our house prices prediction project!
Creating Polynomial Features
One way to enhance our dataset is by creating polynomial features. This involves creating new features that are powers of existing ones. For instance, if we have a feature that represents the number of bedrooms, we could create another feature that's the square of the number of bedrooms.
# Creating a new feature 'Bedrooms_Squared'
df['Bedrooms_Squared'] = df['Bedrooms'] ** 2Interaction Terms
You can also create interaction terms between two different variables. Let's say our dataset has 'Square Footage' and 'Number of Bedrooms'; the interaction would be 'Square Footage per Bedroom.'
# Creating a new feature 'Square_Footage_per_Bedroom'
df['Square_Footage_per_Bedroom'] = df['Square_Footage'] / df['Bedrooms']Categorical Feature Engineering
If you have categorical features that are not capturing the nuances in your data, you can add more granularity. For example, instead of a broad category like 'Neighborhood', you could have 'Close_to_School_in_Neighborhood'.
# Engineering a new feature based on existing categorical features
df['Close_to_School_in_Neighborhood'] = df['Neighborhood'] + "_" + df['Close_to_School'].astype(str)Temporal Features
If your dataset contains date variables, you can extract valuable information from them. For example, if you have the 'Date of Sale', you could extract the 'Month of Sale' or 'Quarter of Sale'.
# Converting 'Date_of_Sale' to datetime format
df['Date_of_Sale'] = pd.to_datetime(df['Date_of_Sale'])
# Extracting the quarter
df['Quarter_of_Sale'] = df['Date_of_Sale'].dt.quarterFeature Transformation
Sometimes, a little mathematical tweak can go a long way. Log transformations are quite useful for dealing with skewed data.
import numpy as np
# Applying log transformation
df['Log_Square_Footage'] = np.log(df['Square_Footage'])Alrighty then! You've successfully engineered some incredibly useful features for your dataset. You might think of this as an optional step, but trust us, good feature engineering can be the difference between a mediocre model and a fantastic one.
In the next section, we'll venture into the thrilling lands of machine learning models, where all your hard work so far will start to pay off. Can't wait to see you there!
Feature Engineering
By now, you've got your data looking all neat and tidy, but guess what? We can make it even better. How, you ask? Through the magic of Feature Engineering!
Feature engineering is essentially the art of enhancing your dataset with new features that could help your model make better predictions. It's like adding spices to a dish to make it even more delicious. Let's walk through this important step in our house prices prediction project!
Creating Polynomial Features
One way to enhance our dataset is by creating polynomial features. This involves creating new features that are powers of existing ones. For instance, if we have a feature that represents the number of bedrooms, we could create another feature that's the square of the number of bedrooms.
# Creating a new feature 'Bedrooms_Squared'
df['Bedrooms_Squared'] = df['Bedrooms'] ** 2Interaction Terms
You can also create interaction terms between two different variables. Let's say our dataset has 'Square Footage' and 'Number of Bedrooms'; the interaction would be 'Square Footage per Bedroom.'
# Creating a new feature 'Square_Footage_per_Bedroom'
df['Square_Footage_per_Bedroom'] = df['Square_Footage'] / df['Bedrooms']Categorical Feature Engineering
If you have categorical features that are not capturing the nuances in your data, you can add more granularity. For example, instead of a broad category like 'Neighborhood', you could have 'Close_to_School_in_Neighborhood'.
# Engineering a new feature based on existing categorical features
df['Close_to_School_in_Neighborhood'] = df['Neighborhood'] + "_" + df['Close_to_School'].astype(str)Temporal Features
If your dataset contains date variables, you can extract valuable information from them. For example, if you have the 'Date of Sale', you could extract the 'Month of Sale' or 'Quarter of Sale'.
# Converting 'Date_of_Sale' to datetime format
df['Date_of_Sale'] = pd.to_datetime(df['Date_of_Sale'])
# Extracting the quarter
df['Quarter_of_Sale'] = df['Date_of_Sale'].dt.quarterFeature Transformation
Sometimes, a little mathematical tweak can go a long way. Log transformations are quite useful for dealing with skewed data.
import numpy as np
# Applying log transformation
df['Log_Square_Footage'] = np.log(df['Square_Footage'])Alrighty then! You've successfully engineered some incredibly useful features for your dataset. You might think of this as an optional step, but trust us, good feature engineering can be the difference between a mediocre model and a fantastic one.
In the next section, we'll venture into the thrilling lands of machine learning models, where all your hard work so far will start to pay off. Can't wait to see you there!