This notebook performs market segmentation using K-means clustering, PCA, and autoencoder methods on sales data.

• Pandas
• Numpy
• Seaborn
• Matplotlib.pyplot
• Tensorflow.keras
• Sklearn.preprocessing
• Sklearn.cluster
• Sklearn.decomposition
• Sklearn.metrics
• Plotly.express
• Plotly.graph_objects

Data Source : Kaggle

The notebook performs simple data cleaning by dropping null columns and converting the ORDERDATE column to datetime format.

The notebook performs the following steps for Exploratory Data Analysis:

• Performing One Hot encoding to all categorical columns.
• Grouping data by order date to perform further analysis.
• Calculating Correlation Matrix.
• Plotting distplots.
• Visualizing the relationship between variables using pairplots.

The notebook performs market segmentation using the following steps:

1. Fit K-means Clustering Model
   • Scale the data.
   • Find the optimal number of clusters using the elbow method.
   • Fit the K-means clustering model using the optimal number clusters (k).
2. Apply Principle Component Analysis
   • Visualize the results.
3. Perform Dimensionality Reduction using the Autoencoder
   • Create an autoencoder model using Keras API.
   • Fit and train the autoencoder model.
   • Use the bottleneck layer (after down-scaling layers and before up-scaling layers) to encode the sales dataframe.
   • Use the elbow method to find the optimal number of clusters.
   • Fit the encoded sales dataframe using K-means clustering model with the optimal number of clusters.
   • Apply Principle Component Analysis and visualize the results.