Project 2

Next Steps:

1. Handle missing values (replace categorical missing values with the mode).
2. Convert categorical data into numerical format for clustering.
3. Apply K-Means Clustering to classify cars into groups.

The Car Dataset has been successfully cleaned and encoded, making it ready for K-Means clustering.

Next Steps:

1. Normalize numerical features to ensure proper clustering.
2. Apply K-Means Clustering with an optimal number of clusters.
3. Predict the class of a given car based on clustering results.

The Elbow Method plot shows that the optimal number of clusters is around 3 or 4, where the inertia (WCSS) starts leveling off.

Next Steps:

1. Apply K-Means Clustering with the optimal number of clusters.
2. Predict the class of a given car based on clustering results.

The Car Dataset has been successfully clustered into 3 groups using K-Means Clustering.

Next Steps:

1. Visualize the clusters to understand car groupings.
2. Predict the class of a new car based on its features.

The visualization of K-Means Clustering results shows how cars have been grouped based on:

1. Engine Size vs. Miles Per Gallon (City)
2. Horsepower vs. Weight

Business Insights from Clustering

• Cluster 0 (Purple): Likely represents mid-range cars with moderate engine size and fuel efficiency.
• Cluster 1 (Blue/Green): Likely represents small & fuel-efficient cars with lower engine sizes and higher MPG.
• Cluster 2 (Yellow): Likely represents high-performance/luxury cars with larger engines, higher horsepower, and lower MPG.

Interpretation of the Prediction:

• Since Cluster 0 consists of mid-range cars with moderate engine size and fuel efficiency, this new car is likely a standard family sedan or a mid-tier vehicle.