Market_Segmentation_model

This repository contains Market Segmentation Model

Market Segmentation Project: Overview and Insights.

1. Understanding Market Segmentation Market segmentation involves dividing a broad consumer or business market into sub-groups of consumers based on some type of shared characteristics. The goal is to tailor marketing strategies and campaigns to these specific segments to better meet their needs and improve sales and customer satisfaction.

2. Data Base The dataset comprises 8,950 entries with detailed information on customer transactions and credit usage. It includes the following columns:

   CUST_ID: Unique identifier for each customer. BALANCE: Current account balance, stored as a float. BALANCE_FREQUENCY: Frequency of balance updates, stored as a float. PURCHASES: Total purchases made, stored as a float. ONEOFF_PURCHASES: Purchases made in a single transaction, stored as a float. INSTALLMENTS_PURCHASES: Purchases made in installments, stored as a float. CASH_ADVANCE: Amount of cash advances taken, stored as a float. PURCHASES_FREQUENCY: Frequency of purchases, stored as a float. ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off purchases, stored as a float. PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases, stored as a float. CASH_ADVANCE_FREQUENCY: Frequency of cash advances, stored as a float. CASH_ADVANCE_TRX: Number of cash advance transactions, stored as an integer. PURCHASES_TRX: Number of purchase transactions, stored as an integer. CREDIT_LIMIT: Credit limit, with one missing value. PAYMENTS: Total payments made, stored as a float. MINIMUM_PAYMENTS: Minimum payments due, with 313 missing values. PRC_FULL_PAYMENT: Percentage of total payments that were full payments, stored as a float. TENURE: Length of the account relationship in months, stored as an integer.

3. Project Steps Data Collection: Gather data relevant to the segmentation bases. Exploratory Data Analysis (EDA): Understand the dataset, identify patterns, and visualize data. Feature Engineering: Create new features or modify existing ones to better represent the data. Segmentation Model: Apply machine learning algorithms to segment the market. Evaluation and Interpretation: Evaluate the segmentation model and interpret the results to derive actionable insights.

4. Exploratory Data Analysis (EDA) and Visualization EDA helps in understanding the dataset, identifying patterns, and discovering anomalies. Visualization tools like Plotly can be used to create interactive and informative graphs.

5. Machine Learning Algorithms for Segmentation K-Means Clustering: Widely used for market segmentation due to its simplicity and efficiency.

6. Deployment Using Streamlit app Download all file while running the app.py (write streamlit run app.py) in your ananconda prompt