Feature Engineering for Improved Housing Price Prediction using Dimensionality reduction , PCA , K-means and Clustering

Introduction

In the world of data science and machine learning, feature engineering plays a crucial role in improving the performance of predictive models. It involves transforming raw data into informative features that enhance the model’s ability to capture patterns and make accurate predictions. In this article, we will explore the concept of feature engineering and apply it to a housing price prediction problem using a California housing dataset. We will cover techniques such as dimensionality reduction, PCA, and scaling, and analyze their impact on model performance.

1. Importing Libraries and Data Collection

We begin by importing the necessary Python libraries, including NumPy, Pandas, and scikit-learn, for data manipulation, analysis, and modeling. Next, we load the California housing dataset, which contains information about various housing features such as location, total rooms, total bedrooms, population, median income, and more.

2. Data Cleaning and Preparation

Before diving into feature engineering, we need to clean and prepare the data. This involves handling missing values and splitting the dataset into training and testing sets. We perform data cleaning by dropping any records with missing values and then proceed to split the dataset into two parts: one for training the model and the other for testing its performance.

3. Exploratory Data Analysis

Before applying feature engineering techniques, it’s essential to perform exploratory data analysis (EDA) to gain insights into the dataset’s characteristics. EDA involves visualizing and summarizing the data to identify patterns, correlations, and potential outliers. By understanding the data better, we can make informed decisions during feature engineering.

4. Baseline Model

To establish a baseline, we create a simple model that predicts housing prices based on the average of the target variable (median house value). This basic model allows us to assess how much improvement the feature engineering techniques bring to the table.

5. Feature Engineering

Now comes the exciting part — feature engineering. We first look into dimensionality reduction, where we consider the correlation between ‘total_rooms,’ ‘total_bedrooms,’ and ‘households.’ By understanding their relationships, we can identify if any of these features can be combined or reduced without losing valuable information.

5.1 Dimensionality Reduction

After observing the correlation, we select ‘total_rooms,’ ‘total_bedrooms,’ and ‘households’ as input features. We then proceed to reduce dimensionality using PCA (Principal Component Analysis). PCA helps us combine features into fewer components while preserving the most important information. We transform the data using PCA and then train a model with these reduced features.

5.2 PCA (Principal Component Analysis)

PCA is a powerful technique for reducing the dimensionality of data. It identifies the principal components that capture the most variance in the data and projects the original features onto a lower-dimensional subspace. By applying PCA, we aim to reduce noise and improve model performance.

5.3 Pre-Processing & Scaling

Another essential step in feature engineering is pre-processing and scaling. Scaling is particularly useful when the variance between feature values is significant. We explore three scaling techniques: StandardScaler, MinMaxScaler, and Normalizer, and analyze their impact on the data.

6. Model Evaluation

After applying feature engineering techniques, we train a random forest regression model using the transformed data. We then evaluate the model’s performance on the test dataset using mean absolute error (MAE) as the evaluation metric. The lower the MAE, the better our model is at predicting housing prices.

7. Conclusion

In this article, we explored the importance of feature engineering in improving predictive models. We applied various techniques, such as dimensionality reduction using PCA and scaling, to preprocess the California housing dataset. By reducing dimensionality and scaling the features, we were able to enhance the model’s performance in predicting housing prices.

Feature engineering is a crucial step in the data science pipeline that empowers models to make more accurate predictions. As data scientists, it is essential to explore and experiment with different feature engineering techniques to optimize model performance and unlock valuable insights from the data.

Feature engineering, when combined with a well-chosen machine learning algorithm, can significantly impact the quality of predictions and is often the key to developing successful and reliable predictive models. By investing time and effort in understanding the data, selecting appropriate features, and applying the right techniques, data scientists can unlock the true potential of their predictive models and drive better decision-making processes in various domains.

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Thank you : )

Regards ;
Darshan Prabhu
Aao Code Kare