Python ANOVA Testing with Statsmodels

Applying ANOVA analysis in Python 📊 Used Statsmodels and Pandas to examine whether Total Liabilities significantly differ based on TAD (Total Asset Dummy) in the financial dataset. Learning how statistical techniques help uncover meaningful financial insights. #Python #DataAnalytics #ANOVA #FinancialAnalysis #LearningByDoing 🚀

One practical habit that improved my data analysis workflow Before starting any analysis, I create a quick data profiling summary In Python using pandas it takes less than a minute 🗯️ This instantly shows: • statistical distribution • missing data ratio • columns with low or high cardinality It helps me detect problems in the dataset before building any model or visualization #DataAnalysis #Python #DataScience

Analyzed global company financial data using Python Found strong relationships between profit, market value, and industry performance. Built ML models with R² = 0.99 to predict company rankings. Built Python #DataAnalytics #DataScience #Python #MachineLearning  #EDA #PortfolioProject #DataVisualization #LinkedInProjects

📊 Exploring Data Relationships using Python! Implemented Correlation Matrix Visualization using Heatmaps and Pair Plots to understand relationships between features in the California Housing Dataset. Also applied Principal Component Analysis (PCA) to reduce dimensionality from 4 features to 2 in the Iris dataset. Tools used: Python | Pandas | Seaborn | Matplotlib | Scikit-learn #DataScience #MachineLearning #Python #DataVisualization #PCA #AI

Claude Add-In for Excel Apparently Claude for Excel is powerful because it uses python execution layer behind the scenes. Instead of forcing everything in a formula it translates everything into a python script. This gives it alot of flexibility to handle messier datasets than formulas and is definately more reliable for complex logic. Its like having a python engine for your spreadsheet, since its release about a month ago I was hooked and have not made another excel formula since. Give it a try its extremely powerful #Anthropic #Claude #Excel #AI #Automation

🏠 Built a Housing Price Prediction model using Linear Regression to estimate property prices based on housing features. Performed EDA, feature analysis, and model evaluation using Python and Scikit-learn. Using data to understand real estate price patterns 📈 #Oasis Infobyte #MachineLearning #Python #DataScience #LinearRegression

🐍 Day 75 — Variance Day 75 of #python365ai 📐 Variance measures how spread out data is. Example: np.var(data) 📌 Why this matters: Variance helps understand how much values differ from the mean. 📘 Practice task: Calculate the variance of a small dataset. #python365ai #Variance #DataScience #Python

📊 Exploring Data with Correlation Analysis! Today I worked on visualizing relationships between different features using a Correlation Heatmap in Python. 🔍 This visualization helps to understand how different variables are related to each other and which features have strong or weak correlations. 💡 Key Insights: ✅ Identified relationships between multiple variables ✅ Observed positive and negative correlations ✅ Useful step for feature selection in Data Analysis & Machine Learning 🛠️ Tools Used: 🐍 Python 📚 Pandas 📊 Seaborn / Matplotlib Data visualization like this helps transform raw data into meaningful insights. #DataScience #Python #DataAnalysis #MachineLearning #DataVisualization #Analytics #LearningJourney

𝐐𝐮𝐢𝐜𝐤 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐨𝐝𝐚𝐲 𝐰𝐡𝐢𝐥𝐞 𝐮𝐬𝐢𝐧𝐠 𝐩𝐚𝐧𝐝𝐚𝐬 While practicing data analysis in Python, I realized how useful groupby() in pandas actually is. At first it looked confusing, but once it clicked, it felt like one of the most practical tools for analysis. You can quickly split data by categories and calculate things like averages, counts, or totals for each group. For example, instead of manually filtering a dataset multiple times, a simple groupby() can show things like average sales per city or number of users per category in seconds. Small function, but it really shows how powerful pandas can be when working with real datasets. #Python #Pandas #DataAnalytics #LearningByDoing

In terms of sharing knowledge on this platform, and given its importance, attached is the table of contents of a reference report on the usage of Machine Learning in Risk Management with Python. If you are interested to receive the full reference report, please drop me a message via Linkedin. #riskmanagement #FRM #Python #machinelearning #ML #financialrisk #riskmodeling