Case Study: You are a data scientist hired by a retail company, "SmartMart," which operates a chain of grocery stores. SmartMart has been in the market for several years and has a significant customer base. However, the company is facing challenges in optimizing its operations and maximizing profits. As a data scientist, your task is to analyze the provided dataset and identify areas where data science techniques can be applied to create business value for SmartMart. Dataset: The dataset provided contains information on SmartMart's sales transactions over the past year. It includes data such as: Date and time of each transaction Customer ID Product ID Quantity sold Unit price Total transaction amount Store ID Tasks: Apply appropriate statistical analysis techniques to extract valuable information from the dataset. This may include but is not limited to:

a. Descriptive statistics b. Correlation analysis c. Hypothesis testing d. Time-series analysis Identify key findings and insights from your analysis that can help SmartMart make data-driven decisions to optimize its operations and increase profitability. Present your analysis results in a clear and concise manner, including visualizations and explanations where necessary. Provide recommendations on specific strategies or actions that SmartMart can take based on your analysis. Deliverables: 1. Written report documenting your analysis process, findings, and recommendations containing Python code/scripts used for data analysis, along with comments explaining the code logic and methodology and also Visualizations (e.g., plots, charts) supporting your analysis and findings. Note: Please provide a single report that includes screenshots of Python code along with corresponding results, as well as screenshots of visualizations that supports your analysis. Dataset: Use the below program to generate dataset with 1000 rows and following 7 columns. Customer ID Product ID Quantity sold Unit price Total transaction amount Store ID import pandas as pd import numpy as np import random

from datetime import datetime, timedelta # Generate 1000 random dates and times within a specific range start_date = datetime(2023, 1, 1) end_date = datetime(2023, 12, 31) date_times = [start_date + timedelta(seconds=random.randint(0, int((end_date - start_date).total_seconds()))) for _ in range(1000)] # Generate random customer IDs customer_ids = ['C' + str(i).zfill(4) for i in range(1, 1001)] # Generate random product IDs product_ids = ['P' + str(i).zfill(3) for i in range(1, 101)] # Generate random quantities sold quantities_sold = np.random.randint(1, 10, size=1000) # Generate random unit prices unit_prices = np.random.uniform(1, 100, size=1000) # Calculate total transaction amounts total_transaction_amounts = quantities_sold * unit_prices # Generate random store IDs store_ids = ['S' + str(i).zfill(3) for i in range(1, 11)] # Randomly assign store IDs to transactions store_ids = [random.choice(store_ids) for _ in range(1000)] # Create DataFrame data = { 'Date & Time': date_times, 'Customer ID': random.choices(customer_ids, k=1000), 'Product ID': random.choices(product_ids, k=1000), 'Quantity Sold': quantities_sold, 'Unit Price': unit_prices, 'Total Transaction Amount': total_transaction_amounts,

'Store ID': store_ids } df = pd.DataFrame(data) # Convert Date & Time column to datetime format df['Date & Time'] = pd.to_datetime(df['Date & Time']) # Sort DataFrame by Date & Time df = df.sort_values(by='Date & Time') # Reset index df.reset_index(drop=True, inplace=True) # Print DataFrame print(df)

Assessment criteria

1. Analysis Process and Methodology

2. Findings and insights

3. Presentation and clarity

4. Python code and scripts

5. Recommendations