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Customer_Segmentation_K-Means-Clustering
June 3, 2023 – June 3, 2023
Built a clustering model to segment customers using k-means clustering, allowing businesses to identify distinct customer groups based on their purchasing patterns and demographics, enabling targeted marketing strategies and personalized customer experiences.
View ProjectQuiz-App---Backend
June 3, 2023 – June 6, 2023
Built REST APIs using Node.js for a Quiz website, utilizing MongoDB as the database for data storage and retrieval.
View ProjectDiabetes-Early-Prediction
April 22, 2021 – April 22, 2021
Model built using Classification algorithms to predict diabetes at early stage.
View ProjectBank-Loan-Analysis
April 14, 2021 – April 14, 2021
Model using machine learning algorithms to determine loan approval for customers.
View ProjectCustomer_Churn_Logistic_Regression
April 8, 2021 – April 8, 2021
Customer_Churn_Logistic_Regression — GitHub repository
View ProjectService_type_classification_KNN
April 6, 2021 – April 6, 2021
Service_type_classification_KNN — GitHub repository
View ProjectCO2_Emission_Linear_Reg
April 5, 2021 – April 5, 2021
CO2_Emission_Linear_Reg — GitHub repository
View ProjectBike_Demand_Prediction
January 20, 2021 – August 2, 2021
Model built using Regression algorithms to predict the number of bikes required in the city at certain hours of the day.
View ProjectMedsCart-Pharmacy-Website
December 29, 2020 – January 27, 2021
Website built using Django framework with rich features, where customers can buy a wide range of medicines and other products.
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong interest in applying machine learning to diverse problems (customer churn, bike demand, customer segmentation, diabetes prediction, loan analysis). This indicates a proactive and curious mindset, which generally aligns well with a data scientist role requiring continuous learning and problem-solving. The mix of data science and web development projects suggests versatility and a willingness to explore different technical domains.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.