Cleaning Time Series Data for Accurate SARIMA Forecasting

Exploratory Data Analysis Project Stock Market Data I recently completed an Exploratory Data Analysis (EDA) project using Python, where I analyzed historical stock market data. Key areas explored: Price trends & patterns Daily returns distribution Volatility behaviour Correlation insights Through this project, I worked with: • Pandas • Matplotlib / Seaborn • Data Cleaning & Visualization One interesting observation: Daily returns cluster heavily around zero, highlighting how most market movements are small, with occasional extreme volatility. Project link:https://lnkd.in/guaYu2VW #DataScience #Python #EDA #StockMarket #Analytics #MachineLearning

📊 Just completed a comprehensive Data Analysis project building custom Python functions for statistical analysis! Built two powerful tools: - quantDDA() - Extended descriptive statistics with 15+ metrics including outlier detection, skewness, and kurtosis - vizDDA() - Automated visualization grids with smart plot selection and missing data heatmaps Applied them to real-world datasets (restaurant tipping patterns & Titanic passenger data), uncovering interesting insights: ✓ Identified systematic missingness patterns (77% in Cabin field, 21% in Age) ✓ Detected heteroscedasticity in tipping behavior across party sizes ✓ Strong correlation between bill amount and tips Tech stack: Python | pandas | NumPy | SciPy | matplotlib | seaborn The framework is reusable for any dataset - perfect for initial exploratory data analysis before modeling. Check out the code: https://lnkd.in/eQ85zTcP #DataAnalysis #Python #Statistics #DataScience #MachineLearning #DataVisualization #EDA

🚀 Day 2 | 15-Day Pandas Challenge 📊 Find the Shape of a DataFrame (Rows & Columns) Understanding the structure of your dataset is the first step in data analysis. In this challenge, we are given a DataFrame called players: Column Name Type player_id int name object age int position object 🎯 Task: Write a solution to calculate and return: [number of rows, number of columns] 💡 Why This Matters: Knowing the number of rows and columns helps you: Understand dataset size Validate data loading Prepare for data cleaning & transformation Analyze data efficiently 🧠 Hint: In Pandas, the .shape attribute gives you both values instantly! 🔥 Key Skills: Python | Pandas | DataFrame Shape | Data Exploration | Data Analysis #Python #Pandas #DataScience #MachineLearning #DataAnalysis #CodingChallenge #LearnToCode #ProgrammersLife #TechCommunity #Developer #AI #Analytics #DataEngineer #100DaysOfCode #CareerGrowth #Upskill #LinkedInLearning #15DaysOfPandas

Data visualization exists for one simple reason: to help us understand the story hidden inside raw data. Numbers alone rarely explain what’s really happening. Charts and graphs turn those numbers into patterns, trends, and insights that our brains can process quickly. In Python, the most commonly used data visualization libraries are Matplotlib and Seaborn — and they serve different but complementary purposes. 🔹 Use Matplotlib when you need complete control over every aspect of a plot, want to build simple and foundational charts, or need to embed visualizations into custom applications or dashboards. 🔹 Use Seaborn when you’re performing Exploratory Data Analysis (EDA), want statistically meaningful and visually appealing plots with minimal code, or are working directly with Pandas DataFrames. The real power comes from using them together. Seaborn helps you create clean, informative visuals quickly, while Matplotlib allows you to fine-tune details like titles, labels, annotations, and layout. Matplotlib and Seaborn don’t compete — they complement. One gives control. The other gives speed. And together, they help data tell its story 📊   #DataVisualization #Python #EDA #Matplotlib #Seaborn #DataScience #DataAnalytics #DataStorytelling #PythonProgramming

𝗧𝗼𝗽 𝟭𝟬 𝗣𝗮𝗻𝗱𝗮𝘀 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 If you're working with Python for data analysis, mastering a few core Pandas functions can dramatically improve your productivity. Here are 10 essential functions used in most real-world data projects: • pd.read_csv() – Load datasets quickly • df.head() – Preview the first rows • df.info() – Understand structure & data types • df.describe() – Generate summary statistics • df.sort_values() – Sort data efficiently • df.groupby() – Aggregate and analyze groups • df.pivot_table() – Create powerful data summaries • pd.concat() – Combine multiple datasets • df.isnull() / df.fillna() – Handle missing data • df.apply() – Apply custom logic to your data These functions form the foundation of practical data analysis with Python. Which Pandas function do you use the most in your workflow? #Python #DataScience #Pandas #DBT #DreamBigTechnologies #AI #LearnPython

𝐑𝐮𝐧𝐧𝐞𝐫𝐬 𝐀𝐧𝐝 𝐈𝐧𝐜𝐨𝐦𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐚𝐲 35: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 Today’s work focused on cleaning and analyzing a combined runners and income dataset using pandas and NumPy. ✔️ Inspected dataset structure, shape, and missing values ✔️ Handled NaNs by dropping empty rows and imputing remaining values ✔️ Used describe() to summarize data and extract key statistics ✔️ Calculated total miles run using NumPy operations ✔️ Filtered individuals based on income thresholds ✔️ Created and exported a clean subset of the data for reuse This session reinforced the importance of data inspection, basic preprocessing, and targeted filtering before moving into deeper analysis. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #SQL #Learning #ostinatorigore

🚀 New Project Published: US Accidents Analysis using Python As part of my learning journey in Analytics, I recently completed a project analyzing the US Accidents Dataset using Python. In this project, I performed Exploratory Data Analysis (EDA) to uncover patterns in traffic accidents such as peak accident hours, weather impact, and city-level accident trends using Python libraries like NumPy, Pandas, Matplotlib, and Seaborn. The dataset contains millions of accident records from across the United States and helps uncover insights that can support better road safety decisions. (Medium) 📊 Project Highlights • Data cleaning and preprocessing • Exploratory Data Analysis (EDA) • Visualization using Matplotlib & Seaborn • Identifying patterns in accident timing, location, and weather conditions 🔗 Read the full article on Medium: https://lnkd.in/gWUD4uCb I would really appreciate your feedback and suggestions to improve my work as I continue growing in the analytics field. #DataAnalytics #BusinessAnalytics #DataAnalyst #Python #EDA #DataVisualization #NumPy #Pandas #Matplotlib #Seaborn #AnalyticsCommunity #DataScience #LearningInPublic #Medium #Kaggle

Day 09: Beyond the Surface—Mastering Precision Data Selection in Pandas 🐼🎯 Data is only as useful as your ability to find what you need within it. Today, I moved deep into Pandas Indexing, transitioning from simple attribute selection to advanced positional and label-based filtering on Kaggle. Key Technical Takeaways: -The Power of loc vs. iloc: I mastered the distinction between position-based selection (iloc) and label-based selection (loc). A key "gotcha" I learned: while iloc follows standard Python slicing (excluding the end), loc is inclusive. -Logical Slicing: Moving beyond rows and columns, I implemented conditional selection. I can now filter massive datasets using boolean logic. -Dynamic Indexing: I explored how to manipulate the DataFrame index using set_index(), transforming a simple numerical count into meaningful, searchable labels like project titles. -Built-in Selectors: I used isin() and notnull() to my arsenal, allowing for clean, efficient filtering of specific categories and missing values. The ability to "query" data directly in Python is a massive productivity boost! #DataScience #Pandas #Python #Kaggle #DataAnalytics #TechSkills

𝗦𝘁𝗶𝗹𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗶𝘁. While exploring data analytics with Python, I’ve been spending time understanding how visualizations actually affect interpretation This work includes: ✺ Practical use of Matplotlib for data visualization ✺ Creating and comparing bar charts, line charts, histograms, box plots, scatter plots, and pie charts ✺ Applying the figure → axes → plot structure to build visuals correctly ✺ Exploring how data types (categorical, numerical, time-series) affect chart selection ✺ Emphasizing labels, scale, clarity, and readability over heavy styling ✺ Avoiding misleading visual choices and focusing on insight-driven plots Along with the project, I documented my learning process and reasoning behind visualization choices and pushed the related code to GitHub. This helped me build stronger fundamentals in data visualization and become more intentional when working with data in Python. What I Learned About Data Visualization (Medium Article) 🔗 https://lnkd.in/gZ_PsgHY Hands-On Code & Experiments (GitHub Repo) 🔗 https://lnkd.in/gN4zmziC #Python #DataVisualization #Matplotlib #DataAnalytics #DataScience #Analytics #GitHub #Medium