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Data Analyst Guide: Mastering Docker + AWS for Junior Data Analyst CV

Data Analyst Guide: Mastering Docker + AWS for Junior Data Analyst CV

Business Problem Statement

As a junior data analyst, you are tasked with analyzing customer purchase behavior for an e-commerce company. The company wants to identify the most profitable customer segments and optimize their marketing campaigns accordingly. The goal is to increase revenue by 15% within the next quarter.

The company has a large dataset of customer transactions, which is stored in a relational database. The dataset contains information about customer demographics, purchase history, and transaction amounts. The company wants to use data analysis and machine learning to identify the most valuable customer segments and predict future purchase behavior.

The ROI impact of this project is significant, as it can help the company to:

  • Increase revenue by 15% within the next quarter
  • Reduce marketing costs by 10% by targeting the most profitable customer segments
  • Improve customer satisfaction by 20% by offering personalized recommendations

Step-by-Step Technical Solution

Step 1: Data Preparation (pandas/SQL)

First, we need to prepare the data for analysis. We will use pandas to load the data from the relational database and perform some basic data cleaning and preprocessing.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load the data from the relational database
conn = psycopg2.connect(
    host="localhost",
    database="database",
    user="username",
    password="password"
)

cur = conn.cursor()
cur.execute("SELECT * FROM customer_transactions")
data = cur.fetchall()

# Create a pandas dataframe from the data
df = pd.DataFrame(data, columns=["customer_id", "transaction_date", "transaction_amount", "customer_demographics"])

# Perform some basic data cleaning and preprocessing
df["transaction_date"] = pd.to_datetime(df["transaction_date"])
df["customer_demographics"] = pd.get_dummies(df["customer_demographics"])

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop("customer_id", axis=1), df["customer_id"], test_size=0.2, random_state=42)
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Step 2: Analysis Pipeline

Next, we will use scikit-learn to build a random forest classifier to predict the most valuable customer segments.

# Build a random forest classifier
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train)

# Make predictions on the testing set
y_pred = rfc.predict(X_test)

# Evaluate the model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
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Step 3: Model/Visualization Code

We will use matplotlib and seaborn to visualize the results of the analysis.

import matplotlib.pyplot as plt
import seaborn as sns

# Plot a histogram of the transaction amounts
plt.hist(df["transaction_amount"], bins=50)
plt.title("Histogram of Transaction Amounts")
plt.xlabel("Transaction Amount")
plt.ylabel("Frequency")
plt.show()

# Plot a scatter plot of the customer demographics and transaction amounts
sns.scatterplot(x="customer_demographics", y="transaction_amount", data=df)
plt.title("Scatter Plot of Customer Demographics and Transaction Amounts")
plt.xlabel("Customer Demographics")
plt.ylabel("Transaction Amount")
plt.show()
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Step 4: Performance Evaluation

We will use metrics such as accuracy, precision, and recall to evaluate the performance of the model.

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Calculate the precision of the model
precision = precision_score(y_test, y_pred)
print("Precision:", precision)

# Calculate the recall of the model
recall = recall_score(y_test, y_pred)
print("Recall:", recall)
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Step 5: Production Deployment

We will use Docker and AWS to deploy the model to production.

# Create a Dockerfile for the model
dockerfile = """
FROM python:3.9-slim

# Set the working directory
WORKDIR /app

# Copy the requirements file
COPY requirements.txt .

# Install the dependencies
RUN pip install -r requirements.txt

# Copy the model code
COPY model.py .

# Expose the port
EXPOSE 8000

# Run the model
CMD ["python", "model.py"]
"""

# Build the Docker image
docker build -t model .

# Push the Docker image to AWS ECR
aws ecr get-login-password --region us-west-2
docker tag model:latest <account_id>.dkr.ecr.us-west-2.amazonaws.com/model:latest
docker push <account_id>.dkr.ecr.us-west-2.amazonaws.com/model:latest

# Deploy the model to AWS ECS
aws ecs create-cluster --cluster-name model-cluster
aws ecs create-service --cluster model-cluster --service-name model-service --task-definition model-task-def --desired-count 1
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SQL Queries

We will use the following SQL queries to extract the data from the relational database:

-- Create a table to store the customer transactions
CREATE TABLE customer_transactions (
    customer_id INT,
    transaction_date DATE,
    transaction_amount DECIMAL(10, 2),
    customer_demographics VARCHAR(255)
);

-- Insert data into the table
INSERT INTO customer_transactions (customer_id, transaction_date, transaction_amount, customer_demographics)
VALUES (1, '2022-01-01', 100.00, 'Male'),
       (2, '2022-01-02', 200.00, 'Female'),
       (3, '2022-01-03', 300.00, 'Male'),
       (4, '2022-01-04', 400.00, 'Female'),
       (5, '2022-01-05', 500.00, 'Male');

-- Select all data from the table
SELECT * FROM customer_transactions;
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Metrics/ROI Calculations

We will use the following metrics to calculate the ROI of the project:

  • Revenue increase: 15%
  • Marketing cost reduction: 10%
  • Customer satisfaction improvement: 20%

We will use the following formula to calculate the ROI:

ROI = (Gain from investment - Cost of investment) / Cost of investment

Where:

  • Gain from investment = Revenue increase + Marketing cost reduction + Customer satisfaction improvement
  • Cost of investment = Cost of data analysis and machine learning project

Edge Cases

We will consider the following edge cases:

  • Handling missing values in the data
  • Handling outliers in the data
  • Handling imbalanced classes in the data

We will use the following techniques to handle these edge cases:

  • Imputation: replacing missing values with mean or median values
  • Transformation: transforming the data to reduce the effect of outliers
  • Oversampling: oversampling the minority class to balance the classes

Scaling Tips

We will use the following scaling tips to deploy the model to production:

  • Use a cloud-based platform such as AWS to deploy the model
  • Use a containerization platform such as Docker to deploy the model
  • Use a orchestration platform such as Kubernetes to manage the deployment
  • Use a monitoring platform such as Prometheus to monitor the deployment

By following these steps and using these techniques, we can deploy a data analysis and machine learning model to production and achieve a significant ROI impact.

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