Freelance Data Scientists in JammuFreelance Data Scientists in Jammu
ProjectsPeople
Aaqib Bashir's avatar
Aaqib Bashir
Jammu
Data Visualizer: Turning Data into Insights
Data Visualizer: Turning Data into Insights
Cover image for Merchandise Sales Analysis
Merchandise Sales Analysis
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1
Cover image for EV Sales Analysis in India
EV Sales Analysis in India
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1
Cover image for HR Analysis Dashboard
HR Analysis Dashboard
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3
Cover image for Churn Analysis
Churn Analysis
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2
Data Scientist(2)
Abdul Rehman's avatar
Abdul Rehman
Sialkot, Pakistan
AI/ML & Data Specialist | Python, Analytics & Cloud
New to Contra
AI/ML & Data Specialist | Python, Analytics & Cloud
Cover image for Built a machine learning web
Built a machine learning web app that predicts customer churn risk from customer, service, contract, and billing data. The project includes data preprocessing, model training, evaluation, and a Streamlit interface for interactive predictions. Developed with Python, Pandas, NumPy, Scikit-learn, Joblib, and Streamlit.
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44
Cover image for Analyzed e-commerce sales data to
Analyzed e-commerce sales data to identify revenue, profitability, product, category, and regional performance patterns. The project includes data cleaning, exploratory data analysis, KPI analysis, trend identification, and interactive visualizations to support data-driven business decisions. Built the analysis using Python, Pandas, NumPy, Matplotlib, and Seaborn, with a focus on translating raw sales data into actionable business insights.
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27
Cover image for Built an AI-powered agronomy advisor
Built an AI-powered agronomy advisor that uses Retrieval-Augmented Generation (RAG) to provide context-aware agricultural guidance. The application combines document retrieval with Gemini to generate relevant responses, using ChromaDB for vector search and TF-IDF for text-based retrieval. Developed a Streamlit interface to make the system accessible through an interactive web application.
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42
Cover image for Built a machine learning web
Built a machine learning web application for classifying text into three sentiment categories: positive, neutral, and negative. The project covers text preprocessing, feature extraction, model training, evaluation, and interactive prediction through a Streamlit interface. Developed with Python, Pandas, Scikit-learn, and Streamlit, with the trained model integrated for real-time sentiment prediction.
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45
Data Scientist(1)