Unit8’s Post

This is a method to try out

Stop wasting time cleaning messy data, AutoClean it in seconds! Why spend hours manually fixing messy datasets when AutoClean can do it all for you in just one line of code? You can automate data cleaning and transformation with this Python library that takes the headache out of data preprocessing, allowing you to focus on building models and generating insights. But remember while it’s powerful, always validate its output to ensure it aligns with your specific problem. Here’s what it does for you: 📍 Handling of Missing Data Detects missing values and imputes them using mean, median, mode, or custom strategies. 📍 Feature Engineering Automatically creates new features by transforming or combining existing ones while distinguishing between categorical and numerical variables. 📍 Data Type Validation Ensures consistent data types and converts columns to the most appropriate types like integers, floats, or categories. 📍 Outlier Detection and Treatment Identifies outliers and either removes or modifies them to improve model performance. 📍 Scaling and Normalisation Applies scaling techniques to ensure numerical features are on the same magnitude. 📍 Duplicate Detection Locates and removes duplicate rows or columns to reduce redundancy. 📍 Univariate Analysis Examines individual feature distributions to identify skewness or inconsistencies. 📍 Feature Reduction Identifies irrelevant or highly correlated features and flags them for removal. 📍 Encoding Categorical Variables Automatically encodes categorical features with techniques like one-hot or label encoding. 📍 Error Correction Fixes common data entry errors such as inconsistent spellings or formats. 📽️ [Watch the video below to see how this line of code works.] ♻️ Repost so others can try it out. 📍 Auto Captions may not be 100% accurate. #DataScience #Automation #DataCleaning #python #MachineLearning #DataAnalytics #AutoClean #FeatureEngineering #PythonTools

This is exactly how I perform my EDA in seconds with ONE line of code! As a data scientist, working smarter is the key to saving time, scaling your impact, and staying ahead. If this is important to you, automating workflows should be a priority in your learning plan, it simplifies repetitive tasks and maximises productivity. For instance, with just create_report(), I can generate a comprehensive EDA report in seconds, covering everything from univariate stats to correlation matrices. Why do I automate? 📍 It cuts out repetitive tasks and speeds up workflows as mentioned. 📍 It ensures reliable results while reducing human error. 📍 It frees up time for innovative problem-solving and advanced analysis. I also tweak my automation scripts regularly. For this line of code, I’ve written a reusable script that I can run whenever needed. If you're still manually handling tasks you can automate, this is your sign to level up! 🎥 [Watch the video below to see how this line of code works.] ♻️ Repost to help others learn and level up their workflows! P.S Auto captions in the video may not be 100% accurate #DataScience #Automation #EDA #Python #Efficiency #2025Goals #DataAnalytics #MachineLearning

this is a time saver indeed. Using dataprep library (more info: https://lnkd.in/gBYJgHef) create_report(df)

🔍 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐢𝐯𝐨𝐭 𝐓𝐚𝐛𝐥𝐞𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠! In the manufacturing industry, data-driven insights are crucial for staying competitive, and pivot tables are one of the most powerful tools for summarizing and analyzing large datasets. But did you know you can create pivot tables just as easily in Python as in Excel? Here's how Python can help manufacturing teams with data analysis! 🛠️📊 Using pandas, a simple pivot_table function lets us track and analyze data in ways that fuel better decisions. Here are some examples of how pivot tables in Python can benefit the manufacturing process: 📦 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Track stock by location, product type, or supplier—avoiding costly stockouts and overstocking. ⚙️ 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐂𝐨𝐬𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Break down material, labor, and overhead costs by product, line, or batch, identifying opportunities to cut costs. 🔧 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Analyze defect rates by shift, supplier, or product type to pinpoint and reduce quality issues. ⏱️ 𝐃𝐨𝐰𝐧𝐭𝐢𝐦𝐞 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠: Monitor machine downtime by shift or line, helping to optimize maintenance and improve equipment utilization. 👥 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Evaluate delivery timeliness, defect rates, and cost per unit across suppliers to strengthen supply chain efficiency. Pivot tables in Python offer manufacturing leaders the power of scalable, flexible analysis that can be adapted as data evolves—no spreadsheet limits! Try using Python’s pivot tables to unlock new levels of productivity and insight. 🚀 #PythonForManufacturing #DataAnalysis #ManufacturingExcellence #PivotTables #DataDriven #SupplyChainOptimization #QualityControl #DataScienceInIndustry #nikhilanalytics

Exploratory Data Analysis (EDA) is a crucial step in understanding your data, but it can also be time-consuming. 📊 What if you could automate most of it in just one step? Sounds amazing, doesn’t it? ✨ That’s exactly what the create_report() function in Python can do! 💫 I didn't know about it until I came across Ene Ojaide’s post. Check it out to learn more! 💡 ----------------------------------------------- If you found it useful, give a reaction 👍 drop a comment 🗨️ share it with your network ♻️ follow for more content 🔔 #data #dataanalytics

Check out my YouTube tutorial for Decision Tree Classifier 👉 https://lnkd.in/gvG-reT6 Continuing our Data Science journey with a focus on Decision Trees and Random Forest Algorithms in Python, all in just 15 minutes! Excited to explore these powerful algorithms! 🌟 #DataScience #DecisionTrees #RandomForest #Python 🌟 Resources: 👉 Dataset: [https://lnkd.in/gQDic6-g] 👉Python code: https://lnkd.in/g6Dqmcrz Feel free to redirect any questions about the code, dataset, or tutorial to collaborator Mithilesh K.. Let's dive in together! 🚀 Connect 🤝 | React 🤓 | Drop 💬 | Re-share ♻️

Just completed " Experimental Design in Python" Key learning: • Experiment and data setup • Normal data • Factorial and randomized block designs • Covariate adjustment • Picking the right hypothesis test • Post-hoc analysis • P-values, alpha, and test errors • Data storytelling with experiments • Transformations and nonparametric tests

Here is the notebook for my Analysis in Python. My Exploration Include Total sales, profit, and quantity sold, regional sales performance, product return trends and their impact, customer behavior and top salespeople, popular shipping methods. Key Insights -Technology leads all product categories in sales, accounting for 14% of total revenue. -West Region faces the highest product return rates, revealing a potential area for improvement. -Standard Class shipping dominates customer preference, balancing speed and cost. -December stands out as the peak month for sales, driven by holiday demand. Let’s Collaborate! I’d love to hear your feedback: What are your thoughts on the approach I took? What additional analyses or techniques would you consider for this dataset? Check out the full project repository here: https://lnkd.in/dCsECJE2 Let’s connect and discuss how data can revolutionize decision-making! #PythonProgramming #Pandas #JupyterNotebook #DataAnalysis #RetailInsights #BusinessAnalytics

🔮📊 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