Updated the README.md file

Customer Personality Analysis/README.md

@@ -59,7 +59,25 @@ To analyze the dataset of customer's personality and build and train the model o
 
 **CONCLUSION**
 
-Using different clustering models found out the silhouette score, v score and completeness score of the a particular column and the whole dataset.
+About the models implemented:
+1. K-Means:
+   * K-Means is a popular clustering algorithm that separates data into 'k' groups or clusters. It works by minimizing the inertia or within-cluster sum-of-squares.
+2. Mean Shift:
+   * Mean Shift is another clustering algorithm that doesn't require the user to specify the number of clusters a priori. Instead, it starts with an initial guess for the number of clusters, and then iteratively refines these clusters by shifting the cluster centroid towards the mean of the points within the cluster.
+3. DBSCAN:
+   * DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. Unlike k-means, DBSCAN does not require the user to specify the number of clusters a priori. Instead, it starts with an arbitrary point and forms a cluster by including all points that are close to it according to some distance measure (usually Euclidean distance).
+4. Agglomerative Clustering
+   * Agglomerative clustering is a type of hierarchical clustering that builds a hierarchy of clusters by recursively merging or splitting existing clusters. This is in contrast to divisive clustering, where clusters are formed by splitting data into smaller groups.
+
+About the metrics details:
+1. Silhouette Score:
+   * The silhouette score is a measure of how similar an object is to its own cluster compared to other clusters. The silhouette scores range from -1 to 1, where a    high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.
+2. V Score:
+   * The V-measure is a metric used to evaluate the quality of a clustering solution. It takes into account both the homogeneity and completeness of the clustering. The V-measure is the harmonic mean between homogeneity and completeness.
+3. Completeness  Score:
+   * The completeness score is a measure of the ratio of correctly classified items to the total actual positives. It is the proportion of the actual positive instances that were correctly classified. A higher completeness score implies that the clustering algorithm is performing better.
+
+After using the 4 models. We found that the best model in these is `K-Means` because its all metrics score are high as compared to other models score with Silhouette score - 0.30, V Score - 0.26 and Completeness - 0.97. On the second the `Agglomerative Clustering` is good model to use as its score is better as compared to other models with Silhouette score - 0.19, V Score - 0.21 and Completeness - 0.98.
 
 **Pawas Pandey**