Project information
- Category: Python Project
- Title: Regression and Clustering
- Project date: 02 May, 2023
- Project URL: Regression and Clustering
Description
Regression Analysis
Utilizing house price data, I develop predictive models to determine house prices through comprehensive regression analysis. This process involves several critical steps:
1. Data preprocessing, including splitting the dataset into training and testing sets.
2. Constructing predictive models using linear regression algorithms on the training data.
3. Evaluating model performance using various metrics such as Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-Square on the testing dataset.
Clustering Analysis
In another facet of my portfolio, I demonstrate proficiency in cluster analysis using customer data from shopping malls. The objective is to uncover customer segmentation based on income and spending scores. The analysis unfolds through a series of methodical stages:
1. Scaling the data to standardize units across variables.
2. Determining the optimal number of clusters using methodologies like the Elbow and Silhouette methods.
3. Executing the clustering algorithm to categorize customers.
4. Evaluating model efficacy through metrics such as the Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index.