Customer Personality Analysis

Customer Personality Analysis/README.md

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+<h1>Customer Personality Analysis</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the customer's personality on the basis of his daily living.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis
+
+**DESCRIPTION**
+
+To analyze the dataset of customer's personality and build and train the model on the basis of different features and variables.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="graph" src="./Images/histplot.png">
+
+<img alt="graph" src="./Images/figure1.png">
+
+<img alt="graph" src="./Images/figure.png">
+
+<img alt="graph" src="./Images/expenses.png">
+
+<img alt="graph" src="./Images/Images.png">
+
+<img alt="graph" src="./Images/marital_status.png">
+
+
+**MODEL USED**
+
+| Model             | Shiloutte Score | V Score | Completeness |
+|-------------------|-----------------|---------|--------------|
+| K-Means           | 0.30            | 0.26    | 0.97         |
+| Mean Shift        | 0.18            | 0.23    | 0.96         |
+| Agglometric       | 0.19            | 0.21    | 0.98         |
+| DBSCAN            | -0.33           | 0.26    | 0.97         |
+
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which contains 2240 entries in it and having 29 columns in it.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. Seaborn
+
+
+**CONCLUSION**
+
+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**
+

Customer Personality Analysis/requirements.txt

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+matplotlib==3.5.2
+seaborn==0.11.2
+numpy==1.19.2
+pandas==1.4.3