The Importance of Feature Selection in Machine Learning: A Case Study on Diamond Price Prediction

Feature selection is a critical step in the machine learning process. It involves identifying and selecting the most relevant features for use in model construction. This not only enhances model performance but also reduces complexity, mitigates overfitting, and improves interpretability. In this blog, we will explore the significance of feature selection using the Diamond Price Prediction dataset from Kaggle and demonstrate how our software, Analytiqus, streamlines this process.

A complete video tutorial on how to select relevant features with Analytiqus

Why is Feature Selection Important?

1. Improved Model Performance: Selecting the most relevant features ensures that the model focuses on the variables that significantly impact the target variable, leading to more accurate predictions.
2. Reduced Overfitting: By excluding irrelevant or redundant features, feature selection helps prevent the model from capturing noise and overfitting to the training data.
3. Enhanced Interpretability: A model with fewer features is easier to understand and interpret, making it more practical for real-world applications.
4. Lower Computational Cost: Using fewer features reduces the data processing requirements, leading to faster training times and more efficient resource usage.

Case Study: Diamond Price Prediction

Dataset Overview

The Diamond Price Prediction dataset from Kaggle contains information on diamonds, including features such as carat, cut, color, clarity, depth, table, price, and measurements. The goal is to predict the price of a diamond based on these attributes.

Steps in Feature Selection with Analytiqus

1. Data Loading and Exploration: Begin by loading the dataset into Analytiqus. Explore the dataset to understand the features and their distributions.
2. Exploratory Data Analysis: Use Analytiqus to perform EDA to no the variation of one parameter with price change.
3. Correlation Analysis: Use Analytiqus to perform a correlation analysis. This helps identify features that have a strong linear relationship with the target variable (price) and can highlight multicollinearity among features.
4. Feature Importance: Analytiqus can calculate feature importance scores using various algorithms. This step helps in ranking features based on their predictive power.
5. Redundant Feature Removal: Identify and remove redundant features that provide no additional value. For example, features with high correlation coefficients among themselves can be pruned to avoid redundancy.
6. Statistical Tests: Conduct statistical tests to evaluate the significance of each feature. Analytiqus provides tools to perform these tests and identify features that significantly impact the target variable.

Data Import:

Explore dataset in interactive tables

Exploratory Data Analysis

Explore the price variations

Calculate the Feature Importance Score with different Algorithms such as Univariate, RFE, and Tree Based approaches.

Based on the Feature Scores select or Discard Variables.

Model Building with Selected Features

After selecting the most relevant features using Analytiqus, the next step is to build the predictive model. Analytiqus simplifies this process with the following capabilities:

1. Automated Machine Learning (AutoML): Analytiqus offers AutoML functionalities that automate the process of model selection, hyperparameter tuning, and evaluation, ensuring the best possible model is identified.
2. Visualization Tools: Utilize Analytiqus’s visualization tools to gain insights into the model’s performance. Visualizations such as feature importance plots, correlation heatmaps, and prediction error plots help in understanding the model’s behavior.
3. Model Evaluation: Assess the model’s performance using various metrics provided by Analytiqus, such as R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Compare the performance of different models to select the best one.

Let’s Build a highly accurate machine learning model with the selected features after basic onehot encoding and scaling.

Encode and Scale Dataset

Train a machine learning model such as random forest

Model Evaluation on Testing dataset:

Mean Squared Error (MSE): 0.004176132763535638

Mean Absolute Error (MAE): 0.04673827309278319

R-squared (R2): 0.9392004653419472

Results and Interpretation

By focusing on the most relevant features, the predictive model for diamond prices becomes more accurate and efficient. The selected features, such as x, y, carat, cut, color, and clarity, are critical determinants of a diamond’s price while features like depth, table, and z are either redundant or have less importance in terms of price prediction. The reduced feature set also makes the model easier to interpret and understand, providing valuable insights into the factors driving diamond prices.

Conclusion

Feature selection is a pivotal step in the machine learning pipeline, offering numerous benefits including improved model performance, reduced overfitting, enhanced interpretability, and lower computational cost. Using the Diamond Price Prediction dataset, we have demonstrated how feature selection can be effectively performed with Analytiqus. Our software simplifies the process, providing tools for data exploration, feature importance ranking, and model building. By leveraging Analytiqus, data scientists and analysts can build robust, efficient, and interpretable models, driving better business decisions and outcomes.

Try Analytiqus today and experience the power of effective feature selection and automated machine learning!

For more information please visit the website link below:

https://aicrux.co.in/