
A wanderlust who happened to sit at home and code :)
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Assessing your cultural and operational fit
Churn_WebApp
June 16, 2026 – Present
Customer churn is one of the most important business challenges for subscription-based companies such as telecom providers, SaaS businesses, streaming platforms, and financial services. This project builds a machine learning-powered web application that predicts customer churn probability and provides business recommendations for customer retention
View Projectsentiment_analysis
June 15, 2026 – Present
Customer reviews contain valuable insights about products, services, and customer satisfaction.In this project, Natural Language Processing (NLP) techniques were used to classify movie reviews as positive or negative using TF-IDF vectorization and machine learning models.
View ProjectFraud_Detection
June 14, 2026 – Present
In this project, machine learning models were used to detect fraudulent credit card transactions using an extremely imbalanced dataset. The project compares: - Logistic Regression - Random Forest - XGBoost and evaluates their effectiveness using fraud-specific metrics such as Precision, Recall, F1-Score, and ROC-AUC.
View ProjectCustomer_Liftime_Value
June 13, 2026 – Present
Customer Lifetime Value (CLV) is one of the most important metrics in retail, e-commerce, and customer relationship management. Rather than treating all customers equally, businesses aim to identify customers who are likely to generate the highest future revenue and allocate marketing resources accordingly.
View ProjectMarketing-Campaign-Analytics
June 13, 2026 – Present
Marketing campaigns are a critical tool for increasing customer engagement, conversions, and revenue. However, not every customer responds to marketing efforts. This project analyzes customer behavior, campaign performance, and purchasing patterns to identify factors that influence campaign success and predict whether a customer will convert.
View Projectretail_demand_forecasting
June 12, 2026 – Present
Demand forecasting is one of the most important machine learning applications in retail, e-commerce, supply chain management, and inventory planning. In this project, historical sales data from multiple retail stores was used to predict future sales demand using machine learning techniques.
View ProjectCustomer-Churn-Prediction
June 11, 2026 – Present
Customer churn is one of the most important business challenges faced by telecom, subscription, banking, and e-commerce companies. This project predicts whether a customer is likely to leave a telecom service provider using machine learning techniques and customer behavioral data.
View ProjectRecommendation_Cosine_Corr_Analysis
June 10, 2026 – Present
Movie Recommendation System using MovieLens 100K A Python-based movie recommender that builds a user-movie matrix and generates item-based collaborative filtering suggestions using Pearson correlation and cosine similarity, with analysis in Jupyter Notebook.
View ProjectCustomer-Segmentation-RFM-KMeans
June 9, 2026 – Present
Built a customer analytics solution using RFM Analysis and K-Means Clustering on 397K+ retail transactions. Segmented 4,338 customers into Champions, Loyal, At-Risk, and Lost groups, uncovering that 14.6% of customers generated nearly 49% of total revenue. Developed actionable retention and loyalty strategies based on customer behavior.
View ProjectTitanic-Survival-Prediction
June 8, 2026 – Present
Machine Learning project predicting Titanic passenger survival using Random Forest Classifier.
View ProjectCultural Fit Analysis
The candidate's projects are all personal and heavily focused on core data science applications, which aligns well with a Data Scientist role. However, the lack of team projects, diverse technologies beyond Jupyter Notebook and Python, or experience in different industry contexts limits the assessment of broader cultural fit and adaptability. The projects demonstrate a strong individual drive for learning and application.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions indicate an ability to identify business problems and apply technical solutions, which suggests problem-solving aptitude.