AI & ML Projects

Personalized Recommendation Engine

Project Overview
We developed a personalized recommendation engine tailored for an online media news channel to enhance user experience by offering customized news content, articles, and video recommendations. The system analyzed users' past interactions, reading habits, and preferences, along with the attributes of news items and behavior of similar users. This predictive approach aimed to recommend news content that users found engaging and relevant, integral to keeping viewers informed and connected with current events.
Technologies Used:
Machine Learning Algorithms: Employed collaborative filtering to leverage user-item interactions, identifying patterns to recommend articles liked by similar users. Utilized neural networks to capture complex relationships between user preferences and news features, employing deep learning techniques like CNNs for analyzing image-based content and RNNs for sequential prediction of user interests.
Cloud Computing: Ensured scalability to process extensive user data and news items, accommodating the channel's growing audience. Cloud platforms provided on-demand resources, handling peak news hours efficiently and cost-effectively.
Natural Language Processing (NLP): Analyzed text data from news articles, headlines, and user queries to understand context and sentiments, significantly enhancing the engine's accuracy in matching news content with user preferences.
Implementation Steps:
Data Collection: Aggregated user interaction data, including articles read, time spent on each piece, and engagement metrics (e.g., likes, shares).
User Profiling: Developed detailed user profiles based on interaction history and preferences.
Content Analysis: Used NLP to categorize news content, extract themes, and identify sentiment.
Model Training: Trained models on historical data to identify patterns and predict user preferences.
Recommendation Generation: Implemented algorithms to match users with the most relevant news content.
Feedback Loop: Incorporated user feedback to refine and improve recommendation accuracy over time.
Impact:
The personalized recommendation engine significantly enhanced user engagement and content discoverability for the news channel. By providing tailored news recommendations, we helped the channel increase viewer retention and satisfaction. This led to longer browsing sessions, higher engagement rates, and an uptick in returning users. For the news channel, these improvements translated into increased ad revenue, a broader audience reach, and a competitive edge in the digital news market. Additionally, the recommendation engine facilitated the introduction of users to a wider array of news topics and perspectives, enriching the overall user experience and fostering a well-informed community.

Customer Churn Prediction

Project Details: Predict whether a customer will churn (stop using a service) based on their usage patterns and interaction data. This is crucial for businesses looking to retain customers by identifying at-risk individuals early.
Tools Used: Python, Scikit-learn for machine learning models, Pandas for data manipulation, and a dataset from a Salesforce and survey data
Implementation Details: Use classification algorithms like Random Forest, Gradient Boosting, & Neural Networks to predict churn. The project includes data cleaning, feature engineering, model training and evaluation, and visualization of insights.
Business Impact: Enables targeted customer retention strategies, personalized marketing, and can significantly reduce costs associated with acquiring new customers versus retaining existing ones.

Sentiment Analysis for Customer Feedback

Project Details: Analyzed customer reviews, social media posts, or survey responses to gauge sentiment towards services/brands. This project aimed to uncover insights into customer satisfaction and preferences, informing business decisions.
Tools Used:
Programming Languages & Libraries: Utilized Python, with NLTK PyTorch for model building.
Data Connectors: Employed APIs from social media platforms (Twitter, Facebook) and review sites , alongside Apache Kafka for real-time data streaming and MongoDB for storage.
Visualization Tools: Created visualizations using Matplotlib and interactive dashboards with Power BI.
Implementation Details:
Data Collection: Fetched data using APIs and web scraping, adhering to terms of service.
Data Preprocessing: Cleaned and preprocessed text data, removing noise and applying tokenization and vectorization.
Model Building & Training: Deployed LSTM networks and BERT for sentiment classification, trained on a labeled dataset.
Visualization & Reporting: Developed dashboards highlighting sentiment trends, providing actionable insights.
Business Impact:
Insights and Decision Making: Offered a comprehensive view of customer sentiment, influencing product development and marketing strategies.
Competitive Advantage: Enhanced brand reputation and customer loyalty by responding proactively to customer feedback.
Innovation: Identified trends and customer needs, driving innovation and market competitiveness.

Automated Expense Management System

Project Overview:
The goal was to develop an automated system that enables both businesses and individuals to effortlessly track, categorize, and optimize their expenses in real-time. The system was designed to identify potential savings opportunities automatically, streamlining the expense management process and providing users with valuable insights into their spending behaviors.
Technologies Used
NLP & Machine Learning: For automated categorization of expenses from receipts and bank statements.
Mobile and Web Applications: To provide user-friendly access and instant notifications on spending.
Data Analytics: For insights into spending patterns and personalized savings recommendations.
Implementation Process
Data Collection: Aggregated financial data from bank feeds, receipts, and manual entries.
Expense Categorization: Utilized NLP and machine learning for automatic expense classification, improving over time with user feedback.
Interface Development: Created easy-to-use mobile and web apps for financial tracking and management.
Analytics and Recommendations: Integrated analytics for actionable insights and tailored advice on reducing expenses.
Impact
Reduced Financial Waste: Identified unnecessary expenditures, aiding in cost reduction.
Enhanced Budgeting: Simplified budget tracking and adherence, promoting better financial discipline.
Informed Financial Decisions: Provided valuable insights for smarter spending and savings.
Saved Time: Minimized manual finance management efforts, making expense tracking efficient.

Evaluating the Business Impact of Customer Reviews: A Data-Driven Approach to Purchasing Trends Analysis

The project aimed to understand the impact of customer ratings on the monetary value of purchases within an enterprise. It involved a detailed data exploration and analysis using various statistical techniques such as correlation analysis, decision tree frameworks, and multivariate regression. Key insights included:

Minor Direct Correlation: Analysis indicated a slight relationship between Attend Poll Ratings and purchase amounts, suggesting no direct strong link between customer ratings and spending.
Decision Tree Model Insights: This non-linear model showed that certain rating brackets correlate with specific average purchase amounts but could explain only about 7.15% of the variance in spending based on ratings.
Multivariate Analysis: Incorporated variables like Advisor Names and Program Types, converted into numerical formats via one-hot encoding. The analysis revealed that each unit increase in Attend Poll Rating, while keeping other variables constant, was associated with a $3.22 increase in purchase amount.
Decision Tree Regression: Accounted for a significant 77.04% of variance in expenditures, highlighting the complex interplay between Attend Poll Ratings, Advisor Names, Program Types, and spending. It identified certain advisors and programs with strong correlations, suggesting their influence on customer feedback.
Identification of Key Contributors: The analysis pinpointed top advisors and program types that contributed to 80% of the ratings, providing valuable insights for targeted business strategies.

Sales Data Analysis

Project

Objective

Analyzed historical sales data to optimize sales strategies, inventory management, and increase profitability.

Data Analyzed

Sales Transactions: Included date, product ID, quantity sold, and sales amount.
Product Details: Covered category, list price, and cost.
Customer Information: Encompassed demographics and purchase history.
Regional Data: Included information on sales regions.

Techniques Employed

Trend Analysis: Identified sales trends over time and by category.
Segmentation: Categorized products and customers based on performance.
Profitability Analysis: Assessed profit margins by product and region.
Sales Forecasting: Predicted future sales using models like ARIMA and LSTM.

Tools Utilized

Python: For data manipulation and analysis, employing libraries such as pandas, NumPy, and matplotlib.
SQL: For querying and aggregating data.
Machine Learning Libraries: Used Scikit-Learn for predictive modeling and TensorFlow/PyTorch for neural network models.
BI Tools: Leveraged Power BI and Tableau for creating interactive dashboards and visualizations.

Outcomes Achieved

Uncovered insights into sales trends and seasonality.
Provided recommendations for optimal inventory levels.
Informed targeted marketing and sales strategies.
Developed predictive models for future sales forecasting.