ML-Clustering-Pipeline

This GitHub repository contains a machine learning pipeline that analyzes a dataset of interests grouped by individuals. It includes data preprocessing, K-Means clustering, and evaluation methods like the Elbow Method and Silhouette Score. The code is designed to provide insights into patterns and relationships among different interest categories.

Data Loading and Inspection

• The pipeline starts by importing necessary libraries such as NumPy, pandas, Seaborn, and Matplotlib. It then loads a dataset from a CSV file named 'Interests_group.csv' using pd.read_csv(). The first few rows of the dataset are examined using df.head(), and information about the dataset is checked using df.info(). Additionally, the shape of the dataset is obtained using df.shape.

Handling Missing Values

• Missing values in the dataset are identified and handled. This involves filling missing values with the median of each column using df.fillna(df.median()). Columns where more than 30% of the data is missing are dropped.

Categorical to Numerical Conversion

• Categorical data is converted into numerical format using Label Encoding with LabelEncoder().

K-Means Clustering

• The K-Means clustering algorithm is applied to the dataset. Specific columns (such as 'grand_tot_interests' and 'interest215') are selected, and the K-Means model is fitted with n_clusters=5. The resulting clusters and centroids are plotted using Seaborn and Matplotlib. Additionally, the Silhouette Score is calculated to evaluate the quality of the clustering.

Elbow Method

• The Elbow Method is applied to determine the optimal number of clusters for K-Means. This method helps in finding the appropriate number of clusters by plotting the Within-Cluster Sum of Squares (WCSS) for different numbers of clusters.

This repository serves as a comprehensive guide for implementing a clustering pipeline in machine learning. It provides a structured approach from data preprocessing to evaluation, facilitating easy understanding and implementation of clustering techniques. Feel free to explore the code and adapt it to your specific use cases!