Market Basket Analysis

 Market Basket Analysis

1. Introduction

Objective: Use the Apriori algorithm to discover purchase patterns in transactional data.
Purpose: Provide insights for cross-selling, product placement, and inventory management by identifying frequent itemsets and association rules.

2. Project Workflow

1. Problem Definition:
   - Analyze customer purchase patterns using transaction data.
   - Key questions:
     - Which items are frequently purchased together?
     - What are the strongest association rules among items?
2. Data Collection:
   - Source: Transactional data from a retail store (CSV or database).
   - Fields: Transaction ID, Item ID.
3. Data Preprocessing:
   - Convert transactional data into a format suitable for association rule mining.
4. Model Building:
   - Use the Apriori algorithm to extract frequent itemsets and generate association rules.
5. Evaluation:
   - Assess the quality of rules using metrics like support, confidence, and lift.
6. Deployment:
   - Integrate findings into a business strategy or dashboard.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Association Rule Mining: mlxtend
  - Visualization: Matplotlib, Seaborn

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn mlxtend
```

Step 2: Load and Explore Dataset

Load the transactional dataset:
```
import pandas as pd

data = pd.read_csv("transactions.csv")
print(data.head())
```
Explore transaction patterns:
```
print(data.describe())
```

Step 3: Preprocess Data

Convert the dataset into a format suitable for the Apriori algorithm:
```
from mlxtend.preprocessing import TransactionEncoder

transactions = data.groupby('TransactionID')['ItemID'].apply(list).values.tolist()
te = TransactionEncoder()
te_array = te.fit(transactions).transform(transactions)
df = pd.DataFrame(te_array, columns=te.columns_)
```

Step 4: Apply Apriori Algorithm

1. Find Frequent Itemsets:
```
from mlxtend.frequent_patterns import apriori

frequent_itemsets = apriori(df, min_support=0.01, use_colnames=True)
print(frequent_itemsets)
```
2. Generate Association Rules:
```
from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5)
print(rules)
```

Step 5: Visualize Results

1. Plot the support vs. confidence of rules:
```
import matplotlib.pyplot as plt

plt.scatter(rules['support'], rules['confidence'], alpha=0.5)
plt.title('Support vs Confidence')
plt.xlabel('Support')
plt.ylabel('Confidence')
plt.show()
```
2. Display frequent itemsets as a bar chart:
```
frequent_itemsets.sort_values(by='support', ascending=False).head(10).plot.bar(x='itemsets', y='support')
plt.title('Top 10 Frequent Itemsets')
plt.show()
```

Step 6: Deployment

Integrate the insights into business tools such as dashboards or recommendation systems.
Use tools like Tableau or Streamlit to visualize the results and make them accessible to stakeholders.

5. Expected Outcomes

1. Identification of frequently purchased itemsets.
2. Discovery of association rules with high support, confidence, and lift.
3. Enhanced business strategies for cross-selling and inventory management.

6. Additional Suggestions

- Experiment with different minimum support and confidence thresholds to refine results.
- Use advanced association rule mining algorithms like FP-Growth for large datasets.
- Incorporate customer segmentation to personalize association rules.