you can also see my work on this dataset and all the work i have data in my Kaggle progile in the following link. [https://www.kaggle.com/code/dimitriskatos/usa-price-house]
In the following notebook we achive a 0.94 training accuracy and 0.92 testing accuracy by using Decision Trees Classifier. In this Notebook we will try to predict if a customer is satisfied or not. We will start by doing explanatory analysis to our dataset and then we will prepare the data for modeling. The machine learning algorithms we will be used in this notebook is the following.
- Logistic Regression.
- Regularization Logistic Regression.
- Decission Trees Classifier.
- Random Forest Classifier.
- Gradient Boosting Classifier.
- Artificial Neural Network.
We will also evaluate each model by using different metrics, such as Confusion Matrix, Recall, precision and F1 score. Finally, we will apply PCA for dimensional reduction.