Mall Customer Segmentation Predictor

The use of machine learning can be seen almost everywhere around us, be it Facebook recognizing you or your friends, or YouTube recommending you a video or two based on your history — Machine Learning is everywhere! However, the ‘magic’ of machine learning is not just limited to only these areas. Machine Learning is broadly categorized as Supervised and Unsupervised Learning. Supervised Learning is one in which we teach the machine by providing both independent and dependent variables, for example, Classifying or predicting values. Unsupervised Learning mainly deals with identifying the structure or pattern of the data. In this type of algorithms, we do not have labeled data(or the dependent variable is absent), for example, clustering data, recommendation systems, etc. Unsupervised Learning provides amazing results as one can deduce many hidden relations between different attributes or features. So,In this project i have solved the problem with unsupervised machine learning and k-means algorithm

Libraries Used:

• Numpy
• Pandas
• Matplotlib
• Seaborn
• Sklearn
• Pickle
• Streamlit

WorkFlow

1. Importing Libraries
2. Displays 5 data from dataset
3. Check last 5 rows form our dataset
4. Find shape of our dataset
5. Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement
6. Checking Num Values
7. Get Overall Statistics About Data
8. K-means clustering
9. Elbow method to find optimal number of clusters
10. Elbow method to find optimal number of clusters
11. Checking a value
12. Save The Model

Livelink Dataset