tokakhaled / Instacart-Market-Basket-Analysis

Recommender system that predicts your next order based on your previous purchases. Also, it discuss the association between product purchases.

Instacart-Market-Basket-Analysis 🛒

Project Description

• This project exploits the power of data analysis and machine learning to take business to the next level!
• It's one of the competiting projects in Data Science - Challenge Round 2 Hosted by Dr. Doaa Mahmoud.
• The project consist of 3 sections
  • EDA and Interactive Dashboard by Power BI
  • Predictive Analysis Model
  • Association Rules

Open directly from kaggle Notebooks!

• EDA on instacart data
• Next Order Recommender

EDA and Interactive Dashboard by Power BI 📊

• General Analysis.
• Analyzing Behavour of users: users who always order same products.
• How Time affects the purchasing behaviour of customers?
• Analyzing products
• Analyzing Organic Prodcuts.
• Purchasing behaviour on Departments and Aisles.

Next Order Recommender/Predictor 🍞 🍟 🍩

A predictive analysis model , that predicts the products ordered in users' future order based on each purchasing history. Primary Key is the user-product pair to predict whether will be in the future order or not.

XGBoost Classifier was used.

Features with highest importance used by the model:

• up_orders_since_last_order: measures how long the user hasn't considered buying a specific product.
• up_order_rate_since_first_time: measures the degree a user like a product. It's the ratio by which a user will buy a product from the first moment he/she knew about it.
• prod_reorder_ratio: measures how customers in general like a product.
• user_reorder_ratio: measures how this user is likely to buy something new!

Association Rules 🍌 ➡️ 🍅

Project Challenges 🙀

Next Order Recommender

• Data is sparse, we have very large number of products and of course the customer will have very few in his/her next order. Data is very skewed to the negative class. Class distribution: 90% negative class, 10% positive class.
• First, we've found that there's alot of false negatives, do We changed the threshold to maximize the recall, while keeping the precision above a certain threshold [0.3].
• In ther words, we wanted to reduce, the false negatives, the number of products the model say user won't predict in the future while he/she will actually does. On the other side, it's okay to allow some false positives, when the model recommends a products the user will less likely buy in his/her next order.

Project Organization

./
├── EDA
|     ├── eda-on-instacart-data.ipynb 
|         
├── Model
|     |                                              
|     └── predictive-analysis-model.ipynb 
└── Business Insights 
      ├── Business Questions-Solutions.pdf                                              
      └── Project-Data Description.pdf

Todos

• Applying dynamic thresholding on user's products according to his/her average basket size.
• Applying seperate prediction on strong behaviour users.
• Deploy the Model.

Contributors

Toka Khaled

Noran Hany