Unsupervised Learning - Kmeans Week 11 Challenge
Q1) For categorical data, similarity is often calculated using methods such as:
1. **Hamming Distance**: Measures the number of differing attributes between two categorical data points. Each mismatch counts as a unit distance.
2. **Jaccard Similarity**: Measures similarity between sets by comparing the size of the intersection to the size of the union. It's often used for binary or multi-class categorical data.
3. **Matching Coefficient**: Counts the number of matching attributes between two categorical data points and divides by the total number of attributes.
These methods help quantify how similar or dissimilar categorical data points are based on their attribute values.
Q2) To implement K-means clustering on a "Car dataset" and derive business insights, follow these steps:
1. **Data Preparation**:
- **Load and Clean Data**: Import the car dataset and handle missing values or outliers.
- **Feature Selection**: Choose relevant features (e.g., price, mileage, engine size, age).
2. **Standardize Data**: Normalize or standardize features to ensure that they are on a similar scale, improving the K-means algorithm's performance.
3. **Apply K-means Clustering**:
- **Choose Number of Clusters**: Use methods like the Elbow Method to determine the optimal number of clusters (K).
- **Run K-means**: Fit the K-means algorithm to the standardized data to partition it into K clusters.
4. **Analyze Clusters**:
- **Cluster Profiles**: Examine the characteristics of each cluster (e.g., average price, mileage) to understand the common traits within each group.
- **Visualize Clusters**: Plot clusters to visualize the distribution and separations.
5. **Derive Business Insights**:
- **Target Market Segmentation**: Identify distinct customer segments based on car features, such as high-end luxury cars versus budget-friendly options.
- **Product Positioning**: Tailor marketing strategies and product features based on the cluster profiles.
- **Pricing Strategies**: Develop differentiated pricing strategies for different segments based on their preferences and characteristics.
- **Inventory Management**: Optimize inventory by aligning stock with the demand patterns identified in each cluster.