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**
+
+Using different clustering models found out the silhouette score, v score and completeness score of the a particular column and the whole dataset.
+
+**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