Business Analytics and Intelligence Minor Projects for Practical Learning

Project 1: Sales Performance Dashboard & Profitability Insights

Objective: To design a dynamic analytics dashboard that monitors sales performance, evaluates profitability, and measures the contribution of different products, regions, and customer segments. This will enable businesses to identify top-performing and underperforming areas, refine sales strategies, and improve profitability.

Core Features

• Import and clean raw sales data from CSV, Excel, or MySQL databases
• Analyze sales trends segmented by product, region, and customer segment
• Calculate profitability metrics, including gross margin and net margin
• Track KPIs such as Sales Growth %, Profit Margin %, and Average Order Value
• Enable interactive filters for category, region, and time period to drill down into insights
• Highlight top 10 best-sellers and bottom 10 underperforming products with visual charts

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn, plotly
• Excel: Pivot tables and slicers for initial analysis
• MySQL: For data storage and querying
• Power BI: For creating the interactive dashboard

Learning Outcomes

• Mastering data cleaning and transformation techniques
• Performing KPI calculations and trend analysis
• Writing SQL queries to retrieve filtered and aggregated sales data
• Designing professional Power BI dashboards for business audiences
• Making data-driven decisions to improve sales performance and profitability

Project 2: Customer Segmentation Using RFM & Clustering

Objective: To segment customers based on their buying patterns using the Recency, Frequency, and Monetary (RFM) model and apply clustering algorithms to personalize marketing strategies.

Core Features

• Calculate RFM scores for each customer using transaction history
• Apply clustering techniques such as K-Means or Hierarchical Clustering
• Categorize customers into groups such as High Value, At-Risk, and Churned
• Visualize customer segments with scatter plots, heatmaps, and distribution charts
• Export segmented lists for targeted marketing campaigns

Tech Stack

• Python: pandas, numpy, scikit-learn, matplotlib, seaborn
• Excel: For initial scoring and validation
• MySQL: To merge transaction and customer data
• Power BI: For visual representation of segments

Learning Outcomes

• Implementing the RFM model for customer analytics
• Applying clustering algorithms in Python
• Visualizing segment characteristics for actionable insights
• Writing SQL joins to merge customer demographic and transactional data
• Driving personalized marketing using segmentation results

Project 3: Inventory Optimization & Demand Forecasting

Objective: To create a predictive system that forecasts future product demand and optimizes inventory levels to minimize stock-outs and overstocking, thereby improving operational efficiency.

Core Features

• Analyze historical sales data for demand patterns
• Build time-series forecasting models (ARIMA, Prophet)
• Calculate safety stock and reorder points for each product
• Generate automated stock-out risk alerts
• Create a dashboard with forecast charts and inventory KPIs

Tech Stack

• Python: pandas, numpy, statsmodels, prophet, matplotlib
• Excel: For stock management templates
• MySQL: For storing product and sales data
• Power BI: For interactive forecasting dashboards

Learning Outcomes

• Applying forecasting techniques in inventory management
• Performing SQL queries for stock-level reporting
• Integrating forecasts into dashboard solutions
• Reducing operational costs through data-driven supply chain optimization
• Understanding seasonality and demand fluctuations

Project 4: Marketing Campaign ROI Analysis

Objective: To measure the effectiveness of marketing campaigns across multiple channels by calculating ROI, identifying high-performing campaigns, and optimizing budget allocation.

Core Features

• Clean and preprocess campaign performance data
• Calculate KPIs: Click-Through Rate (CTR), Conversion Rate, ROI, Customer Acquisition Cost (CAC)
• Compare campaign results across email, social media, and Paid Ads
• Perform trend analysis for campaign performance over different time periods
• Create a dashboard for real-time campaign monitoring

Tech Stack

• Python: pandas, matplotlib, seaborn
• Excel: For campaign data aggregation
• MySQL: For storing campaign and lead conversion data
• Power BI: For KPI visualization and performance tracking

Learning Outcomes

• Understanding marketing analytics fundamentals
• Writing SQL filters and aggregations for campaign performance
• Creating visual stories from campaign data
• Measuring and comparing channel performance
• Optimizing marketing budgets using data-backed insights

Project 5: Financial Statement Analysis & KPI Dashboard

Objective: To automate the extraction and analysis of financial statements to track critical business KPIs and monitor the financial health of the organization.

Core Features

• Import data from balance sheets, income statements, and cash flow statements
• Calculate financial KPIs such as ROI, ROE, Current Ratio, and Debt-to-Equity Ratio
• Analyze trends in revenue, expenses, and profitability
• Visualize cash flow movements and profitability ratios over time
• Create an interactive financial KPI dashboard

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For preliminary statement formatting
• MySQL: For storing financial records
• Power BI: For financial KPI visualization

Learning Outcomes

• Conducting financial data analysis and KPI computation
• Performing SQL joins to merge multi-source finance data
• Designing financial dashboards for decision-makers
• Understanding financial ratios and their business implications
• Automating reporting for finance departments

Project 6: Customer Journey Analytics & Conversion Path Optimization

Objective: To track and analyze the complete customer journey — from first interaction to final purchase — in order to identify drop-off points, optimize conversion paths, and enhance the overall customer experience. This project helps marketing and sales teams understand which touchpoints contribute most to conversions and which need improvement.

Core Features

• Import customer interaction data from multiple sources (website, email campaigns, social media, CRM)
• Map the customer journey stages: Awareness → Interest → Consideration → Purchase
• Calculate KPIs: Conversion Rate per Stage, Average Time Spent in Stage, Cost per Conversion
• Identify high-drop-off stages and analyze causes
• Perform A/B testing analysis for landing pages or campaign variations
• Create an interactive dashboard showing journey flows, conversion paths, and improvement suggestions

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn, plotly
• Excel: For initial journey mapping and funnel data preparation
• MySQL: To store customer interaction and conversion data
• Power BI: For funnel visualization and path analysis dashboards

Learning Outcomes

• Applying funnel analysis for customer experience optimization
• Writing SQL queries to merge multi-channel customer interaction data
• Identifying and addressing drop-off points in the journey
• Visualizing customer paths and conversion metrics in Power BI
• Making data-backed recommendations for marketing and UX improvements

Project 7: Retail Store Location Analysis Using Demographic Data

Objective: To determine the most profitable locations for retail expansion by analyzing demographic data, historical sales, and competitor locations.

Core Features

• Integrate demographic datasets with historical sales data
• Perform geospatial analysis for scoring potential store locations
• Create heatmaps to highlight high-potential zones
• Calculate KPIs such as Location Profitability Potential and Market Saturation Level
• Generate scenario-based recommendations for store selection

Tech Stack

• Python: pandas, geopandas, folium, matplotlib
• Excel: For initial location data cleanup
• MySQL: To store sales and location datasets
• Power BI: For geospatial visualization

Learning Outcomes

• Applying geospatial analytics to retail decision-making
• Combining public demographic data with sales records
• Writing SQL joins for multi-source datasets
• Visualizing geographic business opportunities
• Making data-backed location expansion strategies

Project 8: Supply Chain Cost Analysis & Optimization

Objective: To evaluate supply chain costs at every stage and implement optimization strategies to improve efficiency and reduce expenses.

Core Features

• Break down costs for procurement, transportation, and warehousing
• Identify cost leakages and inefficiencies
• Perform cost-to-serve analysis per customer or product
• Track procurement and logistics expenses over time
• Generate optimization recommendations with cost-saving projections

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For cost breakdown templates
• MySQL: For storing supply chain transaction data
• Power BI: For KPI visualization and optimization tracking

Learning Outcomes

• Understanding supply chain analytics fundamentals
• Building cost optimization models
• SQL aggregation for multi-stage cost breakdowns
• Designing dashboards for operational cost control
• Measuring business impact of optimization initiatives

Project 9: HR Analytics – Employee Performance & Retention Dashboard

Objective: To monitor workforce performance, measure employee engagement, and analyze retention trends for better HR planning.

Core Features

• Calculate performance scores using KPIs such as productivity, quality, and efficiency
• Analyze attrition trends by department, age group, and tenure
• Measure employee satisfaction index
• Identify correlations between performance levels and retention rates
• Create an interactive HR KPI dashboard

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For HR data organization
• MySQL: For storing employee profiles and performance data
• Power BI: For HR metrics visualization

Learning Outcomes

• Preparing HR datasets and calculating employee KPIs
• Writing SQL queries for HR performance reports
• Visualizing HR analytics for leadership teams
• Applying predictive insights for retention planning
• Driving workforce strategy using data

Project 10: E-commerce Recommendation System

Objective: To design and implement a product recommendation engine that personalizes suggestions for customers based on their purchase and browsing history.

Core Features

• Preprocess purchase history and browsing history data
• Implement collaborative filtering and content-based filtering algorithms
• Generate personalized product recommendations
• Track KPIs such as Click-Through Rate (CTR) and Conversion Rate from recommendations
• Integrate recommendation results into a business intelligence dashboard

Tech Stack

• Python: pandas, numpy, scikit-learn, surprise, matplotlib
• MySQL: For storing customer-product interactions
• Power BI: For visualizing recommendation performance

Learning Outcomes

• Understanding recommendation system algorithms
• Writing SQL queries for customer-product association analysis
• Measuring performance of recommendation models
• Visualizing sales uplift from personalization
• Applying personalization strategies for e-commerce growth

Project 11: Dynamic Pricing Model for E-commerce

Objective: To develop an intelligent pricing engine for an e-commerce business that automatically adjusts product prices in real time based on multiple factors such as customer demand, competitor pricing, and stock availability. The goal is to maximize revenue while maintaining competitiveness in the market.

Core Features

• Import and clean historical sales and pricing data from Excel/MySQL
• Integration of competitor pricing data (via APIs or CSV uploads)
• Demand forecasting using time-series analysis to predict product demand in future periods
• Calculation of optimal prices based on competitor price range, inventory levels, and historical elasticity
• Automatic flagging of products with pricing opportunities
• Dashboard with real-time price monitoring, demand trends, and revenue projections

Tech Stack

• Python: pandas, numpy, statsmodels, scikit-learn, matplotlib, seaborn
• Excel: For raw data cleaning and preliminary analysis
• MySQL: To store sales, pricing, and competitor data
• Power BI: For interactive price trend and recommendation dashboards

Learning Outcomes

• Understanding dynamic pricing strategies and their business value
• Applying demand forecasting models for business optimization
• Writing SQL queries to retrieve pricing and sales insights
• Building real-time pricing dashboards in Power BI
• Linking pricing decisions directly to revenue improvement

Project 12: Financial Risk Analysis & Stress Testing

Objective: To evaluate a company’s financial stability under different business and economic conditions by building a stress testing model. This will help businesses prepare for adverse events such as revenue drops, cost increases, or interest rate hikes.

Core Features

• Import and standardize multi-year financial statement data
• Define business and economic risk scenarios (e.g., 15% revenue drop, 10% cost rise)
• Apply scenario models to measure impacts on KPIs like Net Profit Margin, Debt-to-Equity, and Cash Reserves
• Generate risk heatmaps showing which business areas are most vulnerable
• Create dashboards for finance teams with scenario-based results and recommendations

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For baseline financial modeling
• MySQL: To store and query financial history data
• Power BI: For scenario analysis dashboards and visualizations

Learning Outcomes

• Mastering financial risk modeling for real-world business use
• Using scenario analysis to simulate adverse events
• Writing SQL for financial data extraction and transformations
• Designing risk dashboards for quick decision-making
• Translating data insights into actionable financial strategies

Project 13: Logistics Route Optimization & Cost Reduction

Objective: To improve delivery efficiency by identifying the most cost-effective delivery routes for logistics and supply chain operations. The system will reduce transportation costs, improve delivery speed, and optimize resource allocation.

Core Features

• Import delivery orders and existing route data
• Calculate distance and cost for each delivery path
• Implement route optimization algorithms (Dijkstra’s Algorithm, Genetic Algorithms)
• Visualize optimized routes on an interactive map
• Create dashboards to monitor delivery performance and cost savings achieved

Tech Stack

• Python: pandas, geopandas, folium, networkx
• Excel: For base-level route and cost data entry
• MySQL: For storing orders, delivery history, and cost data
• Power BI: For route performance and cost analytics

Learning Outcomes

• Applying route optimization algorithms to business problems
• Using geospatial data visualization for logistics insights
• SQL-based delivery and cost data analysis
• Understanding cost-saving strategies through optimization
• Presenting logistics KPIs in an interactive dashboard

Project 14: Workforce Productivity & Shift Scheduling Analytics

Objective: To analyze employee productivity and create optimal shift schedules for maximum efficiency. This helps HR and operations managers ensure proper workload distribution while avoiding overstaffing or employee burnout.

Core Features

• Collect and preprocess employee attendance, task completion, and output quality data
• Calculate productivity metrics for each employee, department, and shift
• Correlation analysis between shift times and output levels
• Predictive workload scheduling to match peak work hours with optimal staffing levels
• Create a dashboard showing productivity trends, overtime, and idle time

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For initial data entry and shift templates
• MySQL: For employee and shift data management
• Power BI: For shift performance and scheduling dashboards

Learning Outcomes

• Understanding HR analytics for workforce planning
• Writing SQL queries for shift and productivity analysis
• Using predictive analytics to forecast staffing needs
• Creating data-driven schedules to improve performance
• Visualizing workforce KPIs in Power BI for decision-makers

Project 15: Retail Promotion Effectiveness Analysis

Objective: To measure the effectiveness of promotional campaigns in driving sales, attracting new customers, and increasing revenue. This project aims to help marketing teams optimize future campaigns for better ROI.

Core Features

• Import campaign and sales data from Excel/MySQL
• Preprocess and merge datasets to link campaign details with sales performance
• Calculate KPIs like Incremental Sales, Sales Lift %, ROI, and Customer Acquisition Rate
• Compare performance during campaign vs. non-campaign periods
• Build an interactive dashboard for marketing teams showing promotional impact

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For campaign planning and basic data aggregation
• MySQL: To store campaign and sales transaction data
• Power BI: For visualization of campaign results and ROI tracking

Learning Outcomes

• Applying marketing analytics to measure campaign success
• Writing SQL queries to link sales and campaign data
• Using statistical analysis to compare different promotional strategies
• Designing dashboards that make campaign performance easy to interpret
• Connecting marketing spend with measurable business growth

Project 16: Predictive Sales Lead Scoring for CRM Systems

Objective: To develop a predictive lead scoring model for sales teams that ranks leads based on their likelihood to convert. This will help prioritize follow-ups and improve sales efficiency.

Core Features

• Clean and merge CRM data with historical sales results
• Engineer features like lead source, engagement frequency, response time, and past purchase history
• Train classification models (Logistic Regression, Random Forest, XGBoost)
• Assign a conversion probability score to each lead
• Create a dashboard showing high-priority leads and overall conversion trends

Tech Stack

• Python: pandas, numpy, scikit-learn, matplotlib, seaborn
• Excel: For initial data review
• MySQL: For lead and sales data storage
• Power BI: For visualization of lead scoring results

Learning Outcomes

• Understanding predictive modeling for sales operations
• SQL joins to merge CRM leads with transaction data
• Model evaluation metrics for classification problems
• Visualizing lead prioritization for sales teams
• Data-driven sales decision-making

Project 17: Budget vs. Actual Financial Performance Analysis

Objective: To compare planned budgets with actual financial performance, identify variances, and help management improve forecasting accuracy.

Core Features

• Import budget and actual expense/revenue data
• Perform variance analysis for each department and cost center
• Highlight significant over- and under-spending
• Trend visualization of variances over multiple periods
• Dashboard with interactive filters for department and time period

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For budget templates and manual data entry
• MySQL: To store financial records
• Power BI: For variance reporting and trend analysis

Learning Outcomes

• Applying variance analysis for financial control
• Writing SQL queries for period-to-period comparison
• Designing finance-focused dashboards in Power BI
• Understanding budget control as a business process
• Data-driven decision-making for cost management

Project 18: Cash Flow Forecasting & Liquidity Management

Objective: To build a predictive cash flow model that helps businesses anticipate liquidity needs, avoid cash shortages, and make informed investment decisions. This project will be especially useful for finance teams to plan payments, collections, and reserves.

Core Features

• Import historical cash inflow and outflow data from Excel/MySQL
• Categorize transactions by operating, investing, and financing activities
• Build a time-series forecasting model (ARIMA, Prophet) to predict future cash flows
• Highlight months with potential liquidity gaps or surpluses
• Create an interactive dashboard to monitor actual vs. forecasted cash flow and liquidity ratios

Tech Stack

• Python: pandas, numpy, statsmodels, prophet, matplotlib, seaborn
• Excel: For initial transaction categorization
• MySQL: To store and query financial transaction history
• Power BI: For real-time cash flow and liquidity dashboards

Learning Outcomes

• Mastering cash flow statement analysis
• Building time-series models for forecasting
• SQL queries for transaction classification and aggregation
• Visualizing liquidity KPIs for finance decision-making
• Strategic planning for cash reserves and investments

Project 19: Market Share Analysis for Competitive Positioning

Objective: To evaluate a company’s position in the market by calculating market share and identifying growth opportunities against competitors. This project helps in strategic planning and competitive intelligence.

Core Features

• Collect company and competitor sales data from industry reports or databases
• Calculate market share percentages for different product categories
• Identify trends in market share over time
• Segment analysis for geography, product type, or customer group
• Dashboard showing market share trends and competitive insights

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For manual data entry and validation
• MySQL: To store multi-year sales and market data
• Power BI: For market share visualizations and trend analysis

Learning Outcomes

• Applying market share calculations to real-world data
• SQL queries for competitor vs. company data comparisons
• Visualizing competitive trends over time
• Understanding competitive positioning strategies
• Using analytics for market expansion decisions

Project 20: Employee Attrition Prediction Model

Objective: To predict which employees are at risk of leaving the organization so HR can take preventive actions and reduce turnover costs.

Core Features

• Import HR datasets including demographics, job role, tenure, salary, and performance scores
• Data preprocessing (handling missing values, encoding categorical variables)
• Train classification models (Logistic Regression, Decision Tree, Random Forest)
• Generate probability scores for each employee’s attrition risk
• Dashboard with attrition trends, high-risk employee lists, and department-level risk insights

Tech Stack

• Python: pandas, numpy, scikit-learn, matplotlib, seaborn
• Excel: For initial HR data cleanup
• MySQL: To store employee profiles and historical attrition data
• Power BI: For visualization of attrition patterns and predictions

Learning Outcomes

• Understanding attrition prediction models in HR analytics
• SQL joins between employee master data and historical records
• Model evaluation metrics for classification problems
• Data visualization for HR decision-making
• Translating predictions into retention strategies

Project 21: Product Launch Performance Analysis

Objective: To measure the success of a new product launch by analyzing sales trends, customer feedback, and market adoption rate in the initial months.

Core Features

• Import sales data before and after product launch
• Track adoption rate, repeat purchase rate, and sales growth
• Analyze customer feedback sentiment from surveys or reviews
• Compare launch performance to past product launches
• Dashboard with launch KPIs, trends, and recommendations for improvement

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn, textblob (for sentiment analysis)
• Excel: For manual sales and feedback entry
• MySQL: To store launch performance data
• Power BI: For visualization of launch success metrics

Learning Outcomes

• Measuring product launch success using analytics
• SQL aggregation for pre- and post-launch data
• Sentiment analysis for customer feedback
• Visualization of adoption curves and KPIs
• Strategic planning for future launches

Project 22: Expense Categorization & Cost Control Dashboard

Objective: To automate expense categorization and provide managers with clear insights into where money is being spent, helping them control unnecessary costs.

Core Features

• Import expense transaction data from accounting software or Excel
• Categorize expenses into business categories (Travel, Marketing, Salaries, Utilities, etc.)
• KPI tracking: Expense % of Revenue, Category-wise cost trends, Monthly spending changes
• Identify categories with cost overruns and suggest reduction strategies
• Dashboard with filters for department, project, and time period

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For initial category mapping
• MySQL: To store expense and category mappings
• Power BI: For expense trend dashboards and insights

Learning Outcomes

• Automating expense categorization
• SQL filtering for department-wise cost analysis
• Visualization of spending trends and KPIs
• Identifying and reducing unnecessary costs
• Linking cost control measures to profitability improvement

Project 23: Cross-Channel Marketing Performance Analysis

Objective: To compare and analyze the performance of multiple marketing channels (Social Media, Email, Paid Ads, SEO, etc.) and identify the most cost-effective channels for customer acquisition.

Core Features

• Import campaign data from different platforms via CSV/API
• Calculate KPIs: Cost per Acquisition (CPA), ROI, Conversion Rate, Customer Lifetime Value (CLV) per channel
• Compare performance of campaigns across channels and time periods
• Identify underperforming channels and reallocation opportunities
• Dashboard with cross-channel performance insights

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For consolidated channel performance tracking
• MySQL: To store campaign and cost data
• Power BI: For visualization of marketing ROI

Learning Outcomes

• Applying multi-channel analytics for marketing optimization
• SQL joins for campaign and customer data
• Visualizing channel performance trends
• Data-driven budget allocation decisions
• Measuring ROI for marketing investments

Project 24: Procurement Spend Analysis & Cost Savings Opportunities

Objective: To analyze procurement spending patterns, identify cost-saving opportunities, and optimize supplier contracts for better financial efficiency.

Core Features

• Import procurement transaction data from Excel/MySQL
• Categorize purchases by department, supplier, and product type
• KPI calculation: Spend per Supplier, Category-wise Spend %, Price Variance
• Detect high-spend categories for negotiation opportunities
• Dashboard showing spend distribution, trends, and savings potential

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For procurement data validation
• MySQL: To store purchase order and supplier data
• Power BI: For spend analysis dashboards

Learning Outcomes

• Understanding spend analysis techniques in procurement
• SQL queries for category- and supplier-level spend reports
• Identifying potential cost reduction areas from historical data
• Data visualization for procurement decision-making
• Linking spend analysis to supplier negotiation strategies

Project 25: Product Lifecycle Performance Analysis

Objective: To track and analyze the performance of products across different stages of their lifecycle — introduction, growth, maturity, and decline — for better product portfolio management.

Core Features

• Import sales and product data from multiple periods
• Classify products into lifecycle stages based on sales trends
• Identify high-growth vs. declining products
• Recommend marketing and pricing strategies for each stage
• Dashboard with lifecycle charts and product stage transitions

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For initial classification of products
• MySQL: To store product history and sales data
• Power BI: For lifecycle performance visualization

Learning Outcomes

• Applying product lifecycle concepts in analytics
• SQL queries for sales performance tracking over time
• Visualizing lifecycle transitions for management
• Strategic planning for product portfolio optimization
• Connecting lifecycle insights to marketing actions

Project 26:  Customer Complaint Analysis & Service Quality Improvement

Objective: To analyze customer complaints, identify service gaps, and recommend improvements for enhancing service quality and customer satisfaction.

Core Features

• Import complaint logs from Excel/MySQL
• Categorize complaints by type, severity, and resolution time
• KPI calculation: Average Resolution Time, Repeat Complaints Rate, Service Recovery Rate
• Identify top complaint categories and root causes
• Dashboard showing complaint trends, resolution performance, and improvement areas

Tech Stack

• Python: pandas, numpy, matplotlib, seaborn
• Excel: For categorization of complaint data
• MySQL: To store customer complaint and resolution records
• Power BI: For complaint analysis dashboards

Learning Outcomes

• Understanding service quality analytics
• SQL joins for complaint and resolution tracking
• Root cause identification from historical patterns
• Visualization of complaint trends and KPIs
• Linking complaint analysis to quality improvement measures

Project 27: AI Chatbot Analytics for Customer Interaction Insights

Objective: To analyze chatbot interaction data to understand customer behavior, identify unanswered queries, and improve automated response accuracy for better user experiences.

Core Features

• Import conversation logs from chatbot systems
• Categorize queries by topic, sentiment, and resolution status
• KPI tracking: Resolution Rate, Average Response Time, Escalation Rate
• Identify most common unanswered questions for script updates
• Dashboard for tracking chatbot performance and customer engagement

Tech Stack

• Python: pandas, numpy, nltk/spacy for NLP, matplotlib, seaborn
• Excel: For tagging and manual review of conversation samples
• MySQL: To store chatbot logs and interaction metadata
• Power BI: For chatbot performance dashboards

Learning Outcomes

• Applying NLP techniques to customer interaction data
• SQL filtering and aggregation for conversation patterns
• Measuring AI-assisted service efficiency
• Visualization of customer sentiment and topics
• Improving chatbot performance with data insights

Project 28: Predictive Inventory Replenishment System

Objective: To build a predictive system that determines when and how much inventory should be reordered, preventing stock-outs and overstock situations.

Core Features

• Import product sales and stock level data
• Time-series demand forecasting for each SKU
• Safety stock and reorder point calculations
• Automated alerts for low stock items
• Dashboard with restocking recommendations and cost projections

Tech Stack

• Python: pandas, numpy, statsmodels, prophet, matplotlib, seaborn
• Excel: For initial stock data and SKU details
• MySQL: To store product, stock, and sales data
• Power BI: For inventory health and restocking dashboards

Learning Outcomes

• Using predictive analytics for supply chain management
• SQL queries for stock monitoring and analysis
• Forecasting models for demand prediction
• Visualization of inventory KPIs
• Operational decision-making for inventory control

Project 29: Sentiment-Driven Stock Market Analysis

Objective: To combine stock market data with news and social media sentiment to forecast potential price movements and guide investment strategies.

Core Features

• Import historical stock price data and financial news headlines
• Apply NLP sentiment analysis on news and tweets
• Correlate sentiment scores with stock price changes
• Predict short-term stock movement probabilities
• Dashboard with sentiment trends, price movements, and trading signals

Tech Stack

• Python: pandas, numpy, yfinance, nltk, textblob, matplotlib, seaborn
• Excel: For manual data checks and trend validation
• MySQL: To store price history and sentiment scores
• Power BI: For visualization of market sentiment and predictions

Learning Outcomes

• Combining financial data with sentiment analysis
• SQL joins between market and sentiment datasets
• Predictive modeling for trading decisions
• Visualization of sentiment-price relationships
• Understanding real-world applications of financial analytics

Project 30: AI-Enhanced Customer Retention Strategy Dashboard

Objective: To create a dashboard that identifies customers at risk of churn and recommends personalized offers or interventions using AI-driven predictions.

Core Features

• Import customer purchase and engagement history
• Train a churn prediction model using classification algorithms
• Recommend personalized offers based on purchase patterns and preferences
• KPI tracking: Retention Rate, Offer Redemption Rate, CLV
• Dashboard with churn risk segmentation and recommended retention actions

Tech Stack

• Python: pandas, numpy, scikit-learn, matplotlib, seaborn
• Excel: For customer segmentation checks
• MySQL: To store customer and churn prediction data
• Power BI: For retention strategy dashboards

Learning Outcomes

• Building churn prediction models for business use
• SQL queries for customer profiling and segmentation
• Linking AI insights to actionable marketing offers
• Visualization of retention KPIs
• Driving business growth through targeted retention strategies