Unit8’s Post

once a demand forecasting model is deployed, downstream business processes take over a forecasting model’s benefits are lost if the inventory management system does not stock the shelves as predicted or if the logistics team fails to deliver on time the real success depends on translating these predictions into effective actions this is where custom metrics come in handy you can define personalised metrics that better align with the nuances of your business needs create a weighted metric that gives more importance to high-demand products or specific regions where timely delivery is crucial. you can estimate it even without ground truth and monitor its behaviour did I mention you can do all of this using two Python functions? if delays or stock shortages in these areas result in significant business losses, monitoring a custom metric like this would alert you early on read more about custom metrics and how to implement them: https://lnkd.in/drtYrX-C

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀: 𝗕𝗼𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆𝗅 🚀 "Tired of manually cleaning data? Discover the power of automation!" 🤔 Unlock the potential of Pandas to streamline your data preprocessing workflow: 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 - Reduced manual effort 🕒 - Increased accuracy 💯 - Improved scalability 🚀 - Enhanced productivity 📈 Automate Data Cleaning Tasks with Pandas 1️⃣ Handling missing values: 𝘥𝘧.𝘥𝘳𝘰𝘱𝘯𝘢() and 𝘥𝘧.𝘧𝘪𝘭𝘭𝘯𝘢() 2️⃣ Data normalization: 𝘥𝘧.𝘢𝘱𝘱𝘭𝘺() and 𝘥𝘧.𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮() 3️⃣ Data standardization: 𝘥𝘧.𝘢𝘴𝘵𝘺𝘱𝘦() and 𝘥𝘧.𝘢𝘱𝘱𝘭𝘺() 4️⃣ Removing duplicates: 𝘥𝘧.𝘥𝘳𝘰𝘱_𝘥𝘶𝘱𝘭𝘪𝘤𝘢𝘵𝘦𝘴() 5️⃣ Data merging and joining: 𝘱𝘥.𝘮𝘦𝘳𝘨𝘦() and 𝘱𝘥.𝘤𝘰𝘯𝘤𝘢𝘵() Get Started! - Explore Pandas documentation and tutorials - Automate your data cleaning workflow today! - Share your favorite Pandas tips in the comments below! 💬 "Efficient data cleaning = Better insights = Smarter decisions" 💡 #datascience #datacleaning #pandas #automation #efficiency #productivity #dataanalysis #machinelearning #datavisualization #python #qualitydata 💻

Just finished crunching 12 months of sales data from Kaggle. Discovered hidden patterns and trends! My GitHub repo is now live with code and insights. Let's dive into the numbers together and uncover business opportunities. #datascience #salesanalysis #python #kaggle #github https://lnkd.in/gRMFwVDv

📊 Optimizing Logistics with Data: A Scientific Approach 📊 Imagine: Real-time insights into your entire supply chain, powered by data analysis. Here's how: 1. Track & Trace: Utilize robust Software de Seguimiento de Pedidos to capture every shipment detail. 2. Data Extraction: Leverage a widely-used data analysis language like Python to extract key metrics from the software. 3. Predictive Analytics: Develop models to anticipate delays, optimize routes, and manage inventory efficiently. The result? 📈 Reduced costs, enhanced efficiency, and a more sustainable supply chain. #Logistics #SupplyChain #DataAnalytics #Python #Optimization #Sustainability #Efficiency #CostReduction 🌎🚚📦 https://lnkd.in/ecvRg7Xb

Have you ever faced the challenge of adapting your data pipelines to new requirements without disrupting the entire workflow? The Factory Pattern could be the answer you're looking for. It's a tried and tested design pattern in object-oriented programming, and here's how it can streamline your data engineering tasks: Key Benefits: - Encapsulation: Conceals the creation logic for better modularity and loose coupling. - Flexibility: Simplifies the integration of new pipeline types. - Maintainability: Facilitates updates and maintenance, minimising codebase complexity. 👨💻 In a previous role, leveraging the Factory Pattern proved crucial for running diverse pipelines for feature engineering. It allowed us to interchange feature sets seamlessly, which was crucial for experimenting with ML models. 🤔 What are your experiences with the Factory Pattern, or other design patterns in data engineering? Have they made your workflows more robust? #DataEngineering #DesignPatterns #Python

I successfully completed the course covering all the intricacies of "Preprocessing for Machine Learning in Python". Here, I focused on understanding the basics of how and when to perform data preprocessing. This essential step in any machine learning project is when you get your data ready for modeling. Between importing and cleaning your data and fitting your machine learning model is when preprocessing comes into play. You'll learn how to standardize your data so that it's in the right form for your model, create new features to best leverage the information in your dataset, and select the best features to improve your model fit. Finally, you'll have some practice preprocessing by getting a dataset on UFO sightings ready for modeling. Here are the sub-concepts that I covered: - Introduction to Data Preprocessing - Standardizing Data - Feature Engineering - Selecting Features for Modelling It was indeed a wonderful experience undergoing the course as I have gained grounds on the functionalities of training and test sets and how they are instrumental to the creation of reliable models for automation. Follow my journey for more insights and guides on mastering data science with Python.

🔮📊 Unlocking the Power of Forecasting with Python 📊🔮 At Bandy and Moot Private Limited, we’re delivering accurate and insightful forecasts to help our clients stay ahead of the curve. Whether it’s predicting sales, market trends, or supply chain demands, we provide the flexibility and precision businesses need to thrive. Our Approach: Data Preparation: We ensure your data is clean and ready for analysis. Custom Solutions: We select the best forecasting methods to suit your business needs. Actionable Insights: Our forecasts are designed to help you make informed, strategic decisions. Real Impact: Retail: Optimized inventory, reducing excess stock. Finance: Improved investment strategies driven by data. Supply Chain: Reduced waste and better resource allocation. Forecasting allows businesses to stay agile and adapt to change. Interested in how this can benefit your company? Let’s connect! 💬 #Forecasting #DataScience #BandyAndMoot #BusinessGrowth #FutureReady #Python #Forecasting #DataScience #BandyAndMoot #TimeSeries #MachineLearning

I’ve released a new Python package called Forecastify. It’s designed to make time series forecasting easier by offering a simple way to use models like ARIMA, SARIMA, and Exponential Smoothing just by simply loading the dataset. Features: ARIMA and SARIMA for handling time series data with and without seasonality. Exponential Smoothing for trend and seasonality. Built-in Optuna support for model optimization (Selects the best params for model). Visualization tools for generating forecast plots. You can install it using: pip install forecastify You can find more details here: https://lnkd.in/d-UKhtPa #DataScience #Forecasting #Python #TimeSeries

I finished DataCamp's cours on "Dimensionality Reduction in Python". I learned several tricks for automatic feature selection and methods for feature extraction like t-SNE and PCA.

📊 Optimizing Logistics with Python & RAD: A Data-Driven Approach 🚀 Imagine: A real-time dashboard showing your supply chain's efficiency, identifying bottlenecks and suggesting optimal routes. 🤯 Using Pandas and RAD, we can: 1. Collect & Clean: Gather data from various sources (e.g., GPS trackers, inventory systems) and cleanse it using Pandas. 2. Analyze & Visualize: Analyze data patterns with Pandas, uncovering hidden insights and visualizing them with interactive dashboards. 3. Optimize & Automate: Implement automated routing algorithms, inventory management, and predictive analytics using RAD, streamlining operations. This data-driven approach: Boosts efficiency: Reduces delivery times, minimizes stockouts, and optimizes resource allocation. Enhances sustainability: Optimizes routes, reducing fuel consumption and carbon footprint. Cuts costs: Reduces transportation expenses, warehouse storage, and inventory waste. #Logistics #SupplyChain #Python #DataAnalytics #RAD #Sustainability #Efficiency #CostOptimization https://lnkd.in/ecvRg7Xb