Usman Attiq
FAST
· 2020
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i16 - 0073
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Academic
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Year
2020
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Career
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Skills
Python, DASH, Plotly, Scikit-Learn, Bootstrap
Verbatim text
The exact text the LLM saw on the page (or the booklet text from the old import).
This is what powers semantic search.
Customer Attrition Analytics This project aims for predicting customer attrition in an effort to retain them. We worked on data which included customers that have already churned (response) and their characteristics / behaviour (predictors) before the customer turnover happened. By fitting a machine learning model that relates the predictors to the response, we predicted the response for existing customers. With the help of this project, an organization can analyse all relevant customer data and develop focused customer retention programs for a good business. Features Include: Predicting whether customer will Churn or Not Feature Importance Report: Calculating the factors affecting customer attrition and their contribution Extracting and Visualizing key information Interactive Data Visualization to provide the user the freedom to perform the analysis on the data Technology Used: Python, DASH, Plotly Scikit-Learn, Bootstrap Supervisor Name: Dr. Hasan Mujtaba Group Members: Ayela Furrokh (i16 - 0030) Usman Attiq (i16 - 0073) Mirza Asad (i16 - 0136)
AI enrichment
Usman Attiq is a student who collaborated on a machine learning project to predict customer attrition using Python, Scikit-Learn, and Dash. The work involved building predictive models, analyzing feature importance, and creating interactive visualizations to support business retention strategies.
Skills (AI)
["Python", "Machine Learning", "Scikit-Learn", "Dash", "Plotly", "Data Visualization", "Feature Engineering", "Predictive Modeling"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2020 (Final Complete) (1).pdfFrom job #23 page 191
Created: 1778126020