In this project, we aim to perform customer market segmentation for a New York City bank. By analyzing customers' spending habits and other factors, we use K-means clustering and PCA to group customers into distinct segments. This enables the bank to launch targeted marketing campaigns, enhancing customer engagement and boosting sales.
Marketing is critical for the growth and sustainability of any business. Marketers strive to build the company’s brand, engage customers, drive sales, and grow revenue. To achieve these goals, it is crucial to understand customer behavior and preferences. By segmenting the market, businesses can tailor their marketing strategies to different customer groups, leading to more effective campaigns and higher customer satisfaction.
A bank in New York City has collected extensive data on their customers over the past six months. The marketing team wants to launch a targeted marketing campaign by dividing the customers into distinct groups based on their spending habits and other relevant features. As a data consultant, you are tasked with performing customer market segmentation using this data. The goal is to identify at least three distinct customer segments to help the bank target their marketing efforts more effectively.
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Understand the Problem Statement and Business Case
- Analyze the business requirements and the need for market segmentation in targeted marketing.
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Import Libraries and Datasets
- Load necessary Python libraries and import the customer data for analysis.
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Visualize and Explore the Dataset
- Perform exploratory data analysis to understand the distribution and relationships of different features.
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Learn about K-means Clustering
- Understand the K-means clustering algorithm, an unsupervised learning technique used for market segmentation.
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Find the Optimal Number of Clusters using the Elbow Method
- Determine the optimal number of clusters by using the Elbow Method to minimize the sum of squared distances.
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Apply K-means Clustering to the Dataset
- Implement the K-means algorithm to cluster the customer data into distinct groups.
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Perform Dimensionality Reduction using PCA
- Use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset and visualize the clusters.
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Apply Deep Learning Techniques with Autoencoders
- Use autoencoders, a type of deep learning model, for advanced dimensionality reduction and feature extraction.
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Visualize Clusters and Analyze Results
- Visualize the clusters and interpret the results to gain insights into customer segments.
To run this project, you need Python 3.x installed. Use the following commands to set up the environment:
git clone https://github.com/ritz-bot/market-segmentation.git
cd market-segmentation
pip install -r requirements.txt