Data analyst

### Project: **Customer Purchase Behavior Analysis**

#### **Objective:**

To analyze customer purchasing behavior to identify trends and insights that can help a retail company improve its marketing and sales strategies.

#### **Steps:**

### 1. **Data Collection:**

- **Dataset**: A CSV file containing transactional data with columns such as `Customer ID`, `Product ID`, `Purchase Date`, `Purchase Amount`, and `Product Category`.

- You can use publicly available datasets from platforms like Kaggle or simulate data.

```python

import pandas as pd

# Load dataset

df = pd.read_csv("customer_data.csv")

```

### 2. **Data Cleaning:**

- Handle missing values, duplicates, and outliers.

```python

# Check for missing values

df.isnull().sum()

# Drop duplicates

df.drop_duplicates(inplace=True)

# Fill missing values (example for Purchase Amount)

df['Purchase Amount'].fillna(df['Purchase Amount'].median(), inplace=True)

# Handle outliers (example using IQR method)

Q1 = df['Purchase Amount'].quantile(0.25)

Q3 = df['Purchase Amount'].quantile(0.75)

IQR = Q3 - Q1

df = df[~((df['Purchase Amount'] < (Q1 - 1.5 * IQR)) | (df['Purchase Amount'] > (Q3 + 1.5 * IQR)))]

```

### 3. **Exploratory Data Analysis (EDA):**

- Perform basic statistics and visualization to understand the data.

```python

import matplotlib.pyplot as plt

import seaborn as sns

# Summary statistics

print(df.describe())

# Visualize the distribution of purchase amounts

sns.histplot(df['Purchase Amount'], bins=30)

plt.title('Distribution of Purchase Amounts')

plt.show()

# Category-wise purchase count

category_counts = df['Product Category'].value_counts()

sns.barplot(x=category_counts.index, y=category_counts.values)

plt.title('Product Category Distribution')

plt.show()

```

### 4. **Customer Segmentation:**

- Use the RFM (Recency, Frequency, Monetary) model to segment customers.

```python

from datetime import datetime

# Recency: Days since the last purchase

df['Purchase Date'] = pd.to_datetime(df['Purchase Date'])

max_date = df['Purchase Date'].max()

df['Recency'] = (max_date - df['Purchase Date']).dt.days

# Frequency: Total number of purchases

freq_df = df.groupby('Customer ID').size().reset_index(name='Frequency')

# Monetary: Total purchase amount

monetary_df = df.groupby('Customer ID')['Purchase Amount'].sum().reset_index(name='Monetary')

# Combine into RFM dataframe

rfm_df = df.groupby('Customer ID').agg({'Recency': 'min', 'Customer ID': 'size', 'Purchase Amount': 'sum'}).reset_index()

```

### 5. **Data Visualization:**

- Create visualizations to present the insights.

```python

# Top 10 customers by purchase amount

top_customers = df.groupby('Customer ID')['Purchase Amount'].sum().nlargest(10)

top_customers.plot(kind='bar')

plt.title('Top 10 Customers by Purchase Amount')

plt.show()

# Correlation heatmap

sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

plt.title('Correlation Heatmap')

plt.show()

```

### 6. **Insights and Recommendations:**

- **Trend**: Identify which product categories generate the most revenue.

- **Customer Segmentation**: Categorize customers by their purchase frequency and monetary value to create targeted marketing strategies.

- **Action**: Recommend offering personalized discounts to high-frequency or high-value customers to boost loyalty.

### 7. **Conclusion:**