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Hello everyone! I’m Grace Egbe, a passionate Data Analyst with expertise in Python, SQL, Tableau, and Machine Learning. I specialize in data-driven decision-making, business intelligence, and visualization.
I recently created a GitHub Portfolio showcasing my work in E-commerce, Sustainability, and Predictive Analytics, and I’d love to get feedback, suggestions, and collaboration opportunities from the data community! 🚀
📌 Objective: Predicting customer churn using machine learning models.
📊 Tech Stack: Python, Scikit-learn, Logistic Regression, Random Forest
🔍 Insights:
✔️ Monthly subscription users are more likely to churn.
✔️ Payment method and customer service interactions impact churn rates.
✔️ Data-driven customer retention strategies suggested.
🔗 View Project
📢 Looking for Feedback & Suggestions!
I’d love to hear your thoughts on:
✅ How can I improve my portfolio for job applications?
✅ What additional projects would make my profile stand out?
✅ Are there any new datasets or case studies you recommend analyzing?
✅ Would you be interested in collaborating on future projects?
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👋 Introduction
Hello everyone! I’m Grace Egbe, a passionate Data Analyst with expertise in Python, SQL, Tableau, and Machine Learning. I specialize in data-driven decision-making, business intelligence, and visualization.
I recently created a GitHub Portfolio showcasing my work in E-commerce, Sustainability, and Predictive Analytics, and I’d love to get feedback, suggestions, and collaboration opportunities from the data community! 🚀
🔗 My GitHub Portfolio Discussion: View My Portfolio
📊 Featured Projects
1️⃣ Air Quality & Health Impact Analysis 🌍💨
📌 Objective: Understanding how air pollution (PM2.5, NO₂) impacts respiratory diseases.
📊 Tech Stack: Python, Pandas, Seaborn, Tableau
🔍 Insights:
✔️ High PM2.5 levels correlate with increased respiratory disease cases.
✔️ Seasonal patterns show worsening air quality in winter.
✔️ Policy recommendations for improving urban sustainability.
🔗 View Project
2️⃣ E-commerce Customer Analytics 🛒📈
📌 Objective: Understanding customer behavior to improve sales & retention strategies.
📊 Tech Stack: Python, SQL, Tableau
🔍 Insights:
✔️ Returning customers contribute 60%+ of total revenue.
✔️ High-value customers prefer specific product categories.
✔️ Personalized marketing strategies increase retention.
🔗 View Project
3️⃣ Customer Churn Prediction 🔮📉
📌 Objective: Predicting customer churn using machine learning models.
📊 Tech Stack: Python, Scikit-learn, Logistic Regression, Random Forest
🔍 Insights:
✔️ Monthly subscription users are more likely to churn.
✔️ Payment method and customer service interactions impact churn rates.
✔️ Data-driven customer retention strategies suggested.
🔗 View Project
📢 Looking for Feedback & Suggestions!
I’d love to hear your thoughts on:
✅ How can I improve my portfolio for job applications?
✅ What additional projects would make my profile stand out?
✅ Are there any new datasets or case studies you recommend analyzing?
✅ Would you be interested in collaborating on future projects?
Let’s connect and learn from each other! 😊
📡 Connect With Me
📊 Portfolio: https://egbe34.github.io/portfolio/
📊 Kaggle: https://www.kaggle.com/graceegbe12
💼 LinkedIn: (https://www.linkedin.com/in/grace-egbe-77820b278/)
📧 Email: (graceegbe3@gmail.com)
I appreciate any feedback and suggestions! Thanks in advance! 🚀🔥
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