Purnangshu Roy

**Problem:**

ABC Food Products is a leading manufacturer of packaged snacks. Despite no changes in pricing or marketing, granola bar sales have declined over the past six months. The management team needs to identify the root cause and develop a plan to address the issue.

**Data:**

* Sales data for 12 months
* Seasonal indices
* Advertising spend (in dollars) for each month
* Online reviews and ratings for each product (1-5)
* Competitor pricing… Show more

**Problem:**

ABC Food Products is a leading manufacturer of packaged snacks. Despite no changes in pricing or marketing, granola bar sales have declined over the past six months. The management team needs to identify the root cause and develop a plan to address the issue.

**Data:**

* Sales data for 12 months
* Seasonal indices
* Advertising spend (in dollars) for each month
* Online reviews and ratings for each product (1-5)
* Competitor pricing data

**Analysis:**

I analyzed the data using Excel. A time series chart showed a clear decline in sales over the past six months. After accounting for seasonality, I found a moderate positive correlation between advertising spend and sales (0.62), suggesting that increased advertising may be contributing to higher sales.

I performed a linear regression analysis and found that for every dollar increase in advertising spend, there is an estimated 2.5-unit increase in granola bar sales.

**Recommendation:**

1. Increase advertising spend by 20% to capitalize on the positive relationship.
2. Focus on improving online reviews by addressing customer complaints and increasing ratings.
3. Monitor competitor pricing and adjust pricing strategy accordingly.
4. Adjust production levels based on seasonal fluctuations to better match demand.

By implementing these recommendations, ABC Food Products can address the decline in granola bar sales and stabilize its market position. Show less

This Java code defines a calculator class. It adds, subtracts, multiplies, and divides pre-set numbers (10 & 5) and prints the results (sum, difference, product,quotient).https://github.com/8319Joy/General_Calculator.git

This Python program creates a client database, visualizes purchase amounts using a bar plot with Matplotlib, and performs an ANOVA to determine if there are significant differences in purchase amounts across different regions. The database includes client ID, name, age, purchase amount, and region.
https://github.com/8319Joy/Health_care_Python-Analysis.git

Loan default prediction is a common problem in the financial industry, as it can help lenders or banks identify borrowers who are likely to default on their loans. This information can be further used to adjust loan terms/conditions, reserve additional funds to cover potential losses, or even deny/refuse loans to high-risk borrowers. In this case study, we will develop a machine learning-based solution to predict loan… Show more

Loan default prediction is a common problem in the financial industry, as it can help lenders or banks identify borrowers who are likely to default on their loans. This information can be further used to adjust loan terms/conditions, reserve additional funds to cover potential losses, or even deny/refuse loans to high-risk borrowers. In this case study, we will develop a machine learning-based solution to predict loan default.https://github.com/8319Joy/ML---Loan-defaulter-prediction-case-study.git Show less

Objective: To demonstrate how Python can be used to analyze and optimize a personal investment portfolio to maximize returns and minimize risk.Scenario: John Doe has a diversified investment portfolio comprising stocks, bonds, and mutual funds. He wants to optimize his portfolio to achieve the best possible returns while minimizing risk. Steps: Data Collection: Gather historical price data for the assets in the portfolio. Collect each asset's relevant financial metrics (e.g., returns… Show more

Objective: To demonstrate how Python can be used to analyze and optimize a personal investment portfolio to maximize returns and minimize risk.Scenario: John Doe has a diversified investment portfolio comprising stocks, bonds, and mutual funds. He wants to optimize his portfolio to achieve the best possible returns while minimizing risk. Steps: Data Collection: Gather historical price data for the assets in the portfolio. Collect each asset's relevant financial metrics (e.g., returns, volatility). Data Analysis:
Calculate the historical returns and volatility for each asset. Compute the correlation matrix to understand how the assets move in relation to each other. Portfolio Optimization: Use the Modern Portfolio Theory (MPT) to construct an optimal portfolio. Implement the Efficient Frontier to identify the best risk-return trade-offs. Use the Sharpe Ratio to determine the optimal portfolio mix. Visualization: Create visualizations to show the performance of the optimized portfolio. Plot the Efficient Frontier and highlight the optimal portfolio. Simulation: Perform a Monte Carlo simulation to understand the potential future performance of the portfolio under different market conditions. Show less

This program calculates and displays the simple interest, compound interest, and maturity value of a mutual fund based on the principal amount, rate of return, and time in years. You can run this program, enter the required details, and it will display the calculated values. Note: The rate of return should be entered as a percentage, e.g., for 10% enter 10.

To create an Excel sales analysis in Python, you can use libraries such as pandas and openpyxl to read data from Excel files and manipulate it before exporting the results into a new Excel file or CSV file. Here's a simple example using Pandas

Here is a brief description of dashboard preparation through Python:

Python provides various libraries and tools to create interactive and dynamic dashboards. Dashboards are visual representations of data that provide insights and trends. Python libraries such as Dash, Plotly, and Bokeh enable users to create custom dashboards with various features such as charts, graphs, maps, and tables. These libraries allow developers to integrate data sources, create interactive visualizations, and… Show more

Here is a brief description of dashboard preparation through Python:

Python provides various libraries and tools to create interactive and dynamic dashboards. Dashboards are visual representations of data that provide insights and trends. Python libraries such as Dash, Plotly, and Bokeh enable users to create custom dashboards with various features such as charts, graphs, maps, and tables. These libraries allow developers to integrate data sources, create interactive visualizations, and customize the layout and design of the dashboard. Python's ease of use, flexibility, and extensive libraries make it an ideal choice for building complex dashboards. Show less

You can create a simple Python program to generate a professional resume in Anaconda Jupyter Notebook by using the FPDF library, which allows you to create PDF files. Here's an example code where the user can input the details for the resume, and the program generates a well formatted PDF resume.

This Python program defines a Ratio Analysis Calculator to compute key financial ratios for analyzing a company's performance. It calculates:

Current Ratio: Liquidity measure.
Quick Ratio: Liquidity without inventory.
Debt-to-Equity Ratio: Leverage measure.
Return on Assets (ROA): Profitability from assets.
Return on Equity (ROE): Profitability from shareholders' equity.
Gross Profit Margin: Profitability from sales after costs.
Net Profit Margin: Overall profit from… Show more

This Python program defines a Ratio Analysis Calculator to compute key financial ratios for analyzing a company's performance. It calculates:

Current Ratio: Liquidity measure.
Quick Ratio: Liquidity without inventory.
Debt-to-Equity Ratio: Leverage measure.
Return on Assets (ROA): Profitability from assets.
Return on Equity (ROE): Profitability from shareholders' equity.
Gross Profit Margin: Profitability from sales after costs.
Net Profit Margin: Overall profit from sales.
Asset Turnover Ratio: Efficiency in using assets to generate sales.
The program takes company financial data as inputs and outputs these key ratios. Show less

This Python program tracks real-time stock prices for multiple companies using the Alpha Vantage API. It fetches data at 1-minute intervals and plots the stock prices using matplotlib. The program allows users to track multiple stocks simultaneously (e.g., Apple, Google, Microsoft) and save the data to CSV files for future analysis. Additionally, the program includes alert functionality, notifying the user if stock prices exceed specified high or low thresholds. The pandas library processes the… Show more

This Python program tracks real-time stock prices for multiple companies using the Alpha Vantage API. It fetches data at 1-minute intervals and plots the stock prices using matplotlib. The program allows users to track multiple stocks simultaneously (e.g., Apple, Google, Microsoft) and save the data to CSV files for future analysis. Additionally, the program includes alert functionality, notifying the user if stock prices exceed specified high or low thresholds. The pandas library processes the stock data, while real-time plotting visualizes price trends. This program can be further expanded for deeper financial analysis, adding features like volume tracking or more complex alerts. Show less

Explanation:Explanation: Step 1: We define a function extract_job_details to extract relevant details (title, company, location, and summary) from ajob card. Step 1: We define a function extract_job_details to extract relevant details (title, company, location, and summary) from a job card. Step2: The function scrape_jobs accepts a job keyword (e.g., "Business Analytics") and location (e.g., "India"). It sends an HTTP request to Indeed or asimilar job portal and parses the response HTML using… Show more

Explanation:Explanation: Step 1: We define a function extract_job_details to extract relevant details (title, company, location, and summary) from ajob card. Step 1: We define a function extract_job_details to extract relevant details (title, company, location, and summary) from a job card. Step2: The function scrape_jobs accepts a job keyword (e.g., "Business Analytics") and location (e.g., "India"). It sends an HTTP request to Indeed or asimilar job portal and parses the response HTML using BeautifulSoup. Step 2: The function scrape_jobs accepts a job keyword (e.g., "BusinessAnalytics") and location (e.g., "India"). It sends an HTTP request to Indeed or a similar job portal and parses the response HTML usingBeautifulSoup. Step 3: The results are stored in a list and saved to a CSV file using save_to_csv. Step 3: The results are stored in a list and saved toa CSV file using save_to_csv. Output:Output: The code will create a CSV file (jobs.csv) containing job titles, companies, locations, and summaries ofjobs related to Business Analytics in India. The code will create a CSV file (jobs.csv) containing job titles, companies, locations, and summaries of jobs related to Business Analytics in India. Future Enhancements: Future Enhancements: Use APIs like LinkedIn API for fetching more structured job data. Use APIs like LinkedIn API for fetching more structured job data. Add features like filtering based on salary, experience level, or job type. Show less

Here's a brief summary of the report:

The report analyzes the Durga Puja income and expenditure in West Bengal over the last 10 years, comparing it with the state's overall income. The average Durga Puja expenditure has increased from ₹3,500 crore in 2011 to ₹10,000 crore in 2020, with a percentage of 0.11% to 0.83% of the total state income. The market value trend analysis reveals a steady increase in Durga Puja expenditure, with a slight dip in 2014. Python's pandas and matplotlib… Show more

Here's a brief summary of the report:

The report analyzes the Durga Puja income and expenditure in West Bengal over the last 10 years, comparing it with the state's overall income. The average Durga Puja expenditure has increased from ₹3,500 crore in 2011 to ₹10,000 crore in 2020, with a percentage of 0.11% to 0.83% of the total state income. The market value trend analysis reveals a steady increase in Durga Puja expenditure, with a slight dip in 2014. Python's pandas and matplotlib libraries are used to create a line chart to visualize the trend. Show less

Here is a short description of a currency converter calculator:
**Function:** Convert currencies between different currencies.**Inputs:**
* Amount: The amount of money to convert (e.g. $100)
* From currency: The currency to convert from (e.g. USD)
* To currency: The currency to convert to (e.g. EUR)
**Output:** The converted amount in the target currency (e.g. €84.75)
**Features:*** Supports multiple currencies (e.g. USD, EUR, JPY, etc.)
* Updates exchange rates in… Show more

Here is a short description of a currency converter calculator:
**Function:** Convert currencies between different currencies.**Inputs:**
* Amount: The amount of money to convert (e.g. $100)
* From currency: The currency to convert from (e.g. USD)
* To currency: The currency to convert to (e.g. EUR)
**Output:** The converted amount in the target currency (e.g. €84.75)
**Features:*** Supports multiple currencies (e.g. USD, EUR, JPY, etc.)
* Updates exchange rates in real-time
* Allows for easy calculation of conversions
* Provides an estimate of the conversion rate

**Example:**.* Convert $100 USD to EUR
* Enter: 100 USD, EUR
* Output: €84.75 Show less

As a Financial Data Analyst, your goal is to analyze and visualize financial data to help investors make informed decisions. In this project, you will work with a dataset of historical stock prices and other financial metrics to identify trends, patterns, and correlations. You will use Python to clean, preprocess, and analyze the data, and then create visualizations to communicate your findings to stakeholders.

Comparison with Economic Growth:
While the economy grew at a rate of 7.2% in 2017-18 and 7.0% in 2018-19, the per capita income of common man showed a slower growth rate. The rural household income growth was slower than the economic growth rate during these years. The pandemic-induced economic contraction in 2020-21 led to a decline in per capita income for both rural and urban households. However, the economic growth rebounded in 2021-22, and the per capita income also showed an upward… Show more

Comparison with Economic Growth:
While the economy grew at a rate of 7.2% in 2017-18 and 7.0% in 2018-19, the per capita income of common man showed a slower growth rate. The rural household income growth was slower than the economic growth rate during these years. The pandemic-induced economic contraction in 2020-21 led to a decline in per capita income for both rural and urban households. However, the economic growth rebounded in 2021-22, and the per capita income also showed an upward trend.
Data Sources:
Economic growth rates: Reserve Bank of India (RBI) and Ministry of Statistics and Programmed Implementation (MOSPI) data. Per capita income data: National Sample Survey Office (NSSO) data for rural and urban households. Note: The per capita income data is based on the NSSO's consumption expenditure surveys and may not reflect the actual income of every individual. Additionally, the data is subject to revisions and may not reflect the exact figures. Show less

Mutual fund trend analysis in India involves analyzing the performance and behavior of various mutual fund schemes to identify trends, patterns, and correlations. This helps investors make informed investment decisions. Key trends in Indian mutual funds include sectoral allocation, large-cap dominance, value investing, debt funds, and index funds. Technical analysis tools like moving averages and RSI are used to identify trends, while fundamental analysis examines financial ratios. Statistical… Show more

Mutual fund trend analysis in India involves analyzing the performance and behavior of various mutual fund schemes to identify trends, patterns, and correlations. This helps investors make informed investment decisions. Key trends in Indian mutual funds include sectoral allocation, large-cap dominance, value investing, debt funds, and index funds. Technical analysis tools like moving averages and RSI are used to identify trends, while fundamental analysis examines financial ratios. Statistical analysis and machine learning algorithms are also used to identify relationships between fund metrics. By understanding these trends, investors can identify profitable schemes, assess risk, diversify their portfolio, and monitor performance. Show less

Here is a possible HR Data Analytics Dashboard description with problem-solution within 120 words:

**Problem:** HR teams struggle to make data-driven decisions due to limited visibility into employee data, leading to inefficiencies and potential biases in recruitment, talent management, and employee engagement.

**Solution:** Our HR Data Analytics Dashboard provides a centralized platform for tracking and analyzing key HR metrics, such as employee turnover, diversity, and inclusion… Show more

Here is a possible HR Data Analytics Dashboard description with problem-solution within 120 words:

**Problem:** HR teams struggle to make data-driven decisions due to limited visibility into employee data, leading to inefficiencies and potential biases in recruitment, talent management, and employee engagement.

**Solution:** Our HR Data Analytics Dashboard provides a centralized platform for tracking and analyzing key HR metrics, such as employee turnover, diversity, and inclusion metrics. The dashboard offers real-time insights into employee data, allowing HR teams to identify trends, track progress, and make informed decisions. With our dashboard, HR teams can:

* Identify areas for improvement in recruitment and retention
* Monitor diversity and inclusion metrics
* Track employee engagement and sentiment
* Make data-driven decisions to optimize HR processes

This dashboard empowers HR teams to drive business outcomes by leveraging data-driven insights. Show less

Here is a short description of an interest calculator:

**What is an interest calculator?**

An interest calculator is a tool that helps you calculate the amount of interest earned on a loan or investment over a specific period of time. It takes into account the principal amount, interest rate, and time period to provide an estimate of the total interest paid or earned.

**How does it work?**

1. Enter the principal amount (initial amount borrowed or invested)
2… Show more

Here is a short description of an interest calculator:

**What is an interest calculator?**

An interest calculator is a tool that helps you calculate the amount of interest earned on a loan or investment over a specific period of time. It takes into account the principal amount, interest rate, and time period to provide an estimate of the total interest paid or earned.

**How does it work?**

1. Enter the principal amount (initial amount borrowed or invested)
2. Enter the interest rate (percentage)
3. Select the time period (number of years or months)
4. The calculator calculates the total interest paid or earned
5. The result is displayed in a format that shows the principal amount, interest earned, and total amount (principal interest)

**What can you use it for?**

1. Calculate the interest on a loan, such as a mortgage, car loan, or personal loan
2. Determine the return on investment (ROI) for a savings account, certificate of deposit (CD), or other investment
3. Compare different financial products or options to determine which one yields the highest return

**Example:**

Let's say you invest Rs1,000 in a savings account with an annual interest rate of 2% for 5 years. An interest calculator would calculate the total interest earned as $40.12, making the total amount in the account after 5 years Rs1,040.12. Show less

As a Data Analyst at Tablue HR, I dive into the world of numbers to uncover insights that drive business decisions. My primary responsibility is to analyze large datasets to identify trends, patterns, and correlations that can inform HR strategies. I work closely with stakeholders to understand their needs and develop tailored reports that provide actionable recommendations. From analyzing employee demographics to tracking workforce metrics, my analytical skills help organizations optimize… Show more

As a Data Analyst at Tablue HR, I dive into the world of numbers to uncover insights that drive business decisions. My primary responsibility is to analyze large datasets to identify trends, patterns, and correlations that can inform HR strategies. I work closely with stakeholders to understand their needs and develop tailored reports that provide actionable recommendations. From analyzing employee demographics to tracking workforce metrics, my analytical skills help organizations optimize their human capital and make data-driven decisions. With a keen eye for detail and a passion for data storytelling, I empower HR leaders to make informed decisions that drive business success. Show less

Objective:
● Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’

● As a business manager, try to find out the weak areas where you can work to make more profit.

● What all business problems you can derive by exploring the data?

Here is a brief description of a pizza sales analysis report:

**Title:** Pizza Sales Analysis Report

**Objective:** To analyze the sales performance of a pizza business, identifying trends, strengths, and areas for improvement to inform strategic decisions and optimize revenue growth.

**Scope:** The report covers sales data for a specific period (e.g., monthly, quarterly, or annually) and includes information on:

1. **Sales Volume:** Total number of pizzas sold… Show more

Here is a brief description of a pizza sales analysis report:

**Title:** Pizza Sales Analysis Report

**Objective:** To analyze the sales performance of a pizza business, identifying trends, strengths, and areas for improvement to inform strategic decisions and optimize revenue growth.

**Scope:** The report covers sales data for a specific period (e.g., monthly, quarterly, or annually) and includes information on:

1. **Sales Volume:** Total number of pizzas sold, broken down by type (e.g., margherita, pepperoni, veggie) and day of the week.
2. **Revenue:** Total revenue generated from pizza sales, including average sale value and profit margins.
3. **Customer Segmentation:** Analysis of customer demographics, including age, gender, and location, to identify target markets and preferences.
4. **Sales Channels:** Breakdown of sales by channel (e.g., online orders, in-store purchases, delivery), highlighting the most effective channels for sales.
5. **Day-Part Analysis:** Analysis of sales by time of day (e.g., lunch, dinner, late night) to identify peak sales periods and optimize staffing.
6. **Menu Analysis:** Review of menu items and their performance, including top-selling items and those that require adjustments.
7. **Promotions and Discounts:** Evaluation of the effectiveness of promotional activities (e.g., discounts, loyalty programs) on sales and customer retention.
8. **Geographic Analysis:** Breakdown of sales by geographic location (e.g., store location, neighborhood) to identify areas with high demand.
9. **Seasonal Trends:** Analysis of sales patterns over time to identify seasonal fluctuations and opportunities for growth.
10. **Recommendations:** Strategic recommendations for improving sales performance, including menu updates, marketing initiatives, and operational efficiencies.

By analyzing these key metrics, the pizza sales analysis report provides actionable insights to inform business decisions, optimize operations, and drive revenue growth. Show less

Digital Marketing -We are doing ad campaigns, Facebook marketing, Instagram marketing, YouTube marketing, blogging, Instagram project campaign

https://github.com/8319Joy/Python---LAB-4_Unsupervised-Learning---Lab-session4.git

**Business Analytics Project: Customer Segmentation and Retention**

**Objective:** Identify valuable customer segments and develop targeted retention strategies to reduce churn.

**Methodology:**

1. Collected customer data from loyalty program and transactional data.
2. Segmented customers using k-means clustering.
3. Calculated customer lifetime value (CLV) for each segment.
4. Modeled churn probability using logistic regression.
5. Developed targeted retention… Show more

**Business Analytics Project: Customer Segmentation and Retention**

**Objective:** Identify valuable customer segments and develop targeted retention strategies to reduce churn.

**Methodology:**

1. Collected customer data from loyalty program and transactional data.
2. Segmented customers using k-means clustering.
3. Calculated customer lifetime value (CLV) for each segment.
4. Modeled churn probability using logistic regression.
5. Developed targeted retention strategies for high-value segments.

**Results:**

* 3 distinct segments identified: young, middle-aged, and older customers.
* Younger customers have higher spending habits and higher retention rates.
* Churn has a significant impact on revenue and profitability.

**Recommendations:**

* Targeted marketing campaigns for younger customers.
* Loyalty programs and personalized promotions for middle-aged customers.
* Improved customer service for older customers.

**Tools Used:** R, Excel, SQL, Tableau.

**Timeline:** 12 weeks. Show less

Using Python for health prediction analysis involves various statistical and machine learning tools to analyze health data and make predictions. Here's an outline of how you can use Python for health prediction analysis, particularly in the context of India:https://github.com/8319Joy/_Health_-Prediction_-Analysis.git

Python Problem under Data Structure.https://github.com/8319Joy/Python---LAB-2---20-Marks.git

Java description.https://github.com/purnangshu13/G5B4_Lab_Email_Credentials

the Frontend Graded lab 1 of HTML and CSS.https://github.com/8319Joy/HTML--CSS--Lab-1.git

Frontend Graded lab 3 of JavaScript.https://github.com/8319Joy/Lab3_Weather_report.git

car data and analysis.https://github.com/8319Joy/Python_Lab_Project--5.git