- 🌱 At Epython Lab, we specialize in providing Data Engineering, Data Science, and Web Development solutions.
- 💼 Our team is passionate about analyzing datasets, uncovering insights, and developing strategies to help businesses grow.
- 🚀 We have expertise in Python, SQL, Flask, Streamlit, Power BI, Tableau, and Teaching.
- 📚 We are committed to continuous learning and innovation, expanding our capabilities in machine learning, data visualization, and web technologies.
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Description: A machine learning model developed to predict credit risk and assign credit scores, supporting data-driven lending decisions for Bati Bank's Buy-Now-Pay-Later (BNPL) service in collaboration with an eCommerce platform.
Tools Used: Python, Flask, Sklearn, Visualization Tools
Key Features:
- Exploratory Analysis
- RFM Model Development(Customer Risk Classification)
- Machine Learn Model(Predicting Customer Risk)
- Report
Description: This project leverages machine learning to detect fraudulent transactions in e-commerce and banking, aiding in proactive security and risk management. The goal is to provide a robust fraud detection pipeline with explainability, deployment, and dashboard visualization for actionable insights.
Tools Used: Python, Flask, Sklearn, Visualization Tools, Docker, MLflow
Key Features
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Data Analysis & Preprocessing: Handling missing values, data cleaning, and feature engineering for fraud detection.
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Model Building & Training: Comparison of multiple models, including deep learning architectures (CNN, RNN, LSTM).
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Explainability: Interpretation using SHAP and LIME for feature influence insights.
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Deployment: API service for real-time fraud predictions via Flask, Dockerized for scalability.
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Dashboard: Interactive visualization of fraud insights using Dash.
Description: A comprehensive data warehouse solution for Ethiopian medical business data scraped from Telegram channels, including data scraping, object detection with YOLO, and ETL/ELT processes.
Tools Used: Python, Flask, Sklearn, Visualization Tools, dbt(Data Build Tool), PostgreSQL, Telegram API
Key Features
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Scraping data from telegram channels(text, images)
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Cleaning and storing into PostgreSQL
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ETL using DBT
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Object detection using YoloVx
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Deployment: API service for real-time object detection, ETL, and predictions via Flask, Database, and YoloVx.
Description: Building a real-time data ingestion and entity extraction pipeline for Amharic messages from Ethiopian e-commerce Telegram channels. The system leverages fine-tuned Large Language Models (LLMs) to identify key business entities such as product names, prices, and locations. The extracted information is used to populate a centralized platform for EthioMart to streamline e-commerce activities in Ethiopia by consolidating decentralized Telegram channels into a unified hub. The project also includes handling Amharic-specific linguistic features and evaluating model performance for Named Entity Recognition (NER).
Tools Used: Python, Flask, Sklearn, Visualization Tools, Deep Learning
Key Features:
- Extract Amharic Telegram Messages(E-commerce channels)
- Labeling the extracting messages(NER)
- Train the model using Deep Learning(LLM)
- Report)
Description: This project mainly focused on predictive analytics for business. In this project repo, there are 6 different predictive projects you can explore each of them.
Tools Used: Python, Tableau, Alteryx.
Description: This project mainly focused on the GitHub Search Tool, which provides enhanced search functionality and allows users to find repositories based on topics, ratings, and programming languages.
Tools Used: Python, Flask. Key Features:
- Search top-rated GitHub repo
- Search by programming
- Search by Topic
Description: The project is designed to enhance stock market predictions by combining quantitative and qualitative data.
Tools Used: Python, Matplotlib, NLP, etc.
Key Features:
- Sentiment Analysis
- Correlation Analysis
- Financial Quantitative Analysis Project Report
Description: A machine learning solution to forecast sales for Rossmann Pharmaceuticals' stores across various cities six weeks in advance. Factors like promotions, competition, holidays, seasonality, and locality are considered for accurate predictions. The project structure is organized to support reproducible and scalable data processing, modeling, and visualization.
Tools Used: Python, Matplotlib, Seaborn, Tensorflow Scikitlearn, etc.
Key Features:
- Customer Behavior Analysis(EDA)
- Data Preprocessing(Feature Engineering)
- Sales Prediction(RandomForestRegressor)
- Sales Forecasting using a Deep Learning Model Project Report
Description: A project analyzing car insurance claims data to optimize premiums and marketing strategies.
Tools Used: Python, Matplotlib, Seaborn, sci-kit-learn,scipy, shap etc.
Key Features:
- Statistical modeling using Machine Learning Models
- A/B hypothesis testing
- Visualization Project Report
Description: focused on comprehensively analyzing user behavior, engagement, experience, and satisfaction in a telecom dataset.
Tools Used: Python, Matplotlib, sci-kit-learn, etc.
Key Features:
- User Overview Analysis: Analyze handset usage, manufacturers, and application usage.
- User Engagement Analysis: Track engagement across different applications and cluster users based on engagement metrics.
- Experience Analytics: Assess user experience based on network parameters and device characteristics.
- Satisfaction Analysis: Calculate and predict user satisfaction scores based on engagement and experience. Project Report
- 📊 Data-Driven Decisions: We help businesses leverage their data to make smarter decisions.
- 💡 Innovation: We constantly explore new technologies and methodologies to provide cutting-edge solutions.
- 🤝 Collaboration: Our work culture thrives on collaboration with clients and partners to ensure the best outcomes.
At Epython Lab, we are always open to new opportunities and partnerships. Contact us for collaboration, consulting, or any data-driven project needs!