Market data Analysis using K-means Clustering

An Unsupervised machine learning algorithm to create a model with Python

What it is all about?

• Taking a company's dataset about all purchased made and details of customers

• Depending on these data we create Clusters to understand it in a more better way

• Here we have done 5 clusters and given 5 different colours namely,

  1. Red
  2. Green
  3. Blue
  4. Black
  5. Violet

• All the co-ordinates are labelled accordingly. Here I took 3 labels

  1. Customer groups
  2. Spending scores (1-100)
  3. Annual income

• Here we can get the clear picture of customer-sales data.

• Now it can be used to analyse and take a correct decision to increase profit and also user needs.

Packages used

• Numpy
• Pandas
• Seaborn
• Matplotlib
• Sklearn

The Process

The Dataset that I have used for this project is from Kaggle

A little peak into dataset

Checked for any missing data in the csv file, these fill feed false data into our model and we will loose accuracy

• Slicing of multiple columns

x=customer_data.iloc[:,[3,4]].values 

• Finding WCSS value for each clusters and store it for a list

WCSS -> Within Clusters Sum Of Squares Distance b/w each clusters and centroid

we get,

Observe sharp cuttings suggests significant drop

• Training the KMeans model kmeans = KMeans(n_clusters=5,init='k-means++',random_state=0)

• Doing prediction from the trained model, it'll give in ununderstandable format which is list of numbers

• So we scatter all the clusters and their centroids

• Based on x,y coordinate different colours have given to distinguish the clusters easily

• Then using matplotlb we plot the graph like this

Conclusion

• By visualising the data we can understand these like,
  • Blue = less income and less purchase
  • Purple = less income and more purchase
  • Green = more income and less purchase
  • Black = more income more purchase
• Market can attract Blue group people providing some discounts
• Market can attract Green region people who have money but not buying more things

Applications

• Netflix suggesting group of people who are watching some genre more
• Google ads personalisation