One_hot_encoding.py

Explorations\Preprocess\One_hot_encoding\One_hot_encoding.py

This script demonstrates how to preprocess categorical variables and apply linear regression for house price prediction. Key steps include:

1. Data Loading: It loads a dataset of house prices.
2. Dummy Variables: It creates dummy variables for the ‘town’ column using pd.get_dummies() and merges them with the original dataframe.
3. Dummy Variable Trap: It drops one dummy variable to avoid multicollinearity (dummy variable trap).
4. Feature and Target Split: It separates the dataset into features (X) and the target variable (price).
5. Model Training: A Linear Regression model is trained on the data.
6. Predictions: It predicts house prices based on various features and evaluates the model’s accuracy.
7. Label Encoding and One-Hot Encoding: It applies LabelEncoder to convert ‘town’ names into numbers and uses OneHotEncoder to create dummy variables for categorical columns.
8. Final Predictions: It predicts prices using the transformed features and evaluates the model’s performance.