Correlating Financial Data with Pandas

Running statistical analysis with Python 📊 Used Statsmodels to perform ANOVA testing to examine the relationship between Total Liabilities and TAD (Total Asset Dummy) in the financial dataset. Exploring how statistical models help uncover insights from financial data. #Python #DataAnalytics #Statsmodels #FinancialAnalysis #LearningByDoing 🚀

🐍 Day 76 — Standard Deviation Day 76 of #python365ai 📏 Standard deviation shows the typical distance from the mean. Example: np.std(data) 📌 Why this matters: Standard deviation is widely used in statistics and machine learning. 📘 Practice task: Compare standard deviation for two datasets. #python365ai #StandardDeviation #Statistics #Python

🐍 Day 62 — Scatter Plots Day 62 of #python365ai 🎯 Scatter plots reveal relationships between variables. Example: plt.scatter([1,2,3], [2,4,5]) plt.show() 📌 Why this matters: Scatter plots are foundational in regression analysis and ML. 📘 Practice task: Plot two related numeric lists. #python365ai #ScatterPlot #MachineLearning #Python

📌 Selection and Indexing in Pandas Selection and indexing in Pandas are used to access specific data from a DataFrame or Series. They allow us to retrieve particular rows, columns, or subsets of data based on labels or positions. Pandas provides different ways to perform selection and indexing, making it easier to work with large datasets efficiently. These techniques are essential for data exploration, filtering, and analysis when working with structured data. #Python #Pandas #DataAnalytics #DataScience #LearningPython

🧠 Data Analytics Quiz Which chart is best for showing the relationship between two variables? A) Pie Chart B) Bar Chart C) Scatter Plot D) Histogram 💬 Comment your answer below 👉 Swipe to see the correct answer. #DataAnalytics #Python #DataScience #Learning

🐍 Day 78 — Probability Distributions Day 78 of #python365ai 📉 A probability distribution describes how values occur. Common examples: - Normal distribution - Binomial distribution - Uniform distribution 📌 Why this matters: Understanding distributions helps interpret real-world data. 📘 Practice task: Search for examples of normally distributed variables. #python365ai #ProbabilityDistribution #Statistics #Python

Descriptive Statistics (describe()) Caption: 📊 Descriptive Statistical Analysis Generated summary statistics using df.describe() to analyze: • Mean • Standard Deviation • Minimum & Maximum • Distribution trends Statistical summaries provide quick financial insights before advanced modeling. #Statistics #FinancialAnalysis #Python #DataScience

🐍 Day 74 — Mode Day 74 of #python365ai 🔁 The mode is the most frequent value in a dataset. Example: from scipy import stats stats.mode(data) 📌 Why this matters: Mode is useful for categorical data analysis. 📘 Practice task: Find the most common value in a list. #python365ai #Mode #Statistics #Python

Successfully implemented a moderation model using Python, Pandas, NetworkX, and Matplotlib to analyze the impact of Financial Strength and TAD on Total Assets. Built a structural relationship graph to visualize direct and interaction effects (FS × TAD). Exploring how data-driven modeling helps in understanding financial performance dynamics. 📊💡 #DataAnalysis #Python #FinancialModeling #SEM #MachineLearning #Analytics

Developed a simple Linear Regression model to predict real estate values based on year data. This model was built using Python and deployed via a Flask API, enabling predictions through API requests. Tools used: • Python • Scikit-learn • Flask API • NumPy • Postman This project explores the integration of machine learning models into APIs for real-world prediction systems. It has been a valuable learning experience while experimenting with @Uptor. #MachineLearning #Python #FlaskAPI #DataScience #AI #Learning