🚀 Data Cleaning Project – Step 1 I recently started working on a Customer Behavior Analysis dataset as part of my data analytics journey. 🔹 What I did: • Explored the dataset structure and data types • Identified missing values using visualization (heatmap & bar chart) • Handled null values to make the data analysis-ready • Verified duplicate records (no duplicates found) • Removed an irrelevant column 📊 Key Learning: • Not all datasets require heavy cleaning • Data validation is as important as data cleaning • Visualization helps in better understanding of missing data 📌 Attached: Before vs After Data Cleaning visuals ➡️ Next Step: Exploratory Data Analysis (EDA) #DataAnalytics #Python #DataCleaning #Pandas #LearningJourney #StepByStep

Shared my Telecom Customer Churn Analysis Dashboard First time I worked with real data set of about 230k downloaded from kaggle.😊 This project helpe to : ✔️ Understand how data helps identify patterns   ✔️ Learned how to analyze customer churn behavior   ✔️ Gained insights into factors affecting churn   ✔️ Improved ability to interpret real-world data   ✔️ Strengthened analytical thinking through data Built using SQL, Python, and Power BI. Still learning step by step, but this gave me real confidence. Would love to know your thoughts. #KaliyonaSQL #KaliyonaDataAnalytics #KaliyonaWithGayathriBhat

Turning Data into Decisions Recently, I worked on a Sales Order & Cost Analytics Project where I analyzed real-world business data to uncover key insights on revenue, profitability, and performance. Key Highlights: • Identified top-performing products driving maximum revenue • Analyzed channel performance for better business strategy • Built a simple regression model to predict sales • Visualized trends using Python (Matplotlib, Pandas) One key insight: Sometimes, high revenue doesn’t always mean high profit — understanding margins is crucial! This project strengthened my skills in data analysis, visualization, and business decision-making. Excited to keep learning and exploring more in the field of Data Analytics 📈 #DataAnalytics #Python #BusinessAnalytics #LearningByDoing #Projects #CareerGrowth

𝗠𝗼𝘀𝘁 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. 𝘛𝘩𝘦𝘺 𝘢𝘳𝘦 𝘮𝘪𝘴𝘴𝘪𝘯𝘨-𝘷𝘢𝘭𝘶𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴. If you do not handle nulls properly in pandas, your analysis can quietly go off track. Here is a simple way to clean missing values:  1. Find them first • df.isna().sum()  2. Drop them only when it makes sense  • df = df.dropna()  • 𝗨𝘀𝗲𝗳𝘂𝗹 𝘄𝗵𝗲𝗻:  • the number of missing rows is small  • the column is not critical  • removing them will not bias the result  3. Fill them intelligently  A. For numerical columns:     𝘥𝘧["𝘢𝘨𝘦"] = 𝘥𝘧["𝘢𝘨𝘦"].𝘧𝘪𝘭𝘭𝘯𝘢(𝘥𝘧["𝘢𝘨𝘦"].𝘮𝘦𝘥𝘪𝘢𝘯())  B. For categorical columns:     𝘥𝘧["𝘤𝘪𝘵𝘺"] = 𝘥𝘧["𝘤𝘪𝘵𝘺"].𝘧𝘪𝘭𝘭𝘯𝘢(𝘥𝘧["𝘤𝘪𝘵𝘺"].𝘮𝘰𝘥𝘦()[0])  C. For time-based data:    d𝘧["𝘴𝘢𝘭𝘦𝘴"] = 𝘥𝘧["𝘴𝘢𝘭𝘦𝘴"].𝘧𝘧𝘪𝘭𝘭() Here is the real lesson: 𝘋𝘰 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 “𝘳𝘦𝘮𝘰𝘷𝘦 𝘵𝘩𝘦 𝘣𝘭𝘢𝘯𝘬𝘴.” Ask what the missing value means. Sometimes it is: 𝙖 𝙙𝙖𝙩𝙖 𝙚𝙣𝙩𝙧𝙮 𝙞𝙨𝙨𝙪𝙚 𝙖 𝙨𝙮𝙨𝙩𝙚𝙢 𝙜𝙖𝙥 𝙖𝙣 𝙤𝙥𝙩𝙞𝙤𝙣𝙖𝙡 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙖𝙘𝙩𝙞𝙤𝙣 𝙖 𝙨𝙞𝙜𝙣𝙖𝙡 𝙬𝙤𝙧𝙩𝙝 𝙞𝙣𝙫𝙚𝙨𝙩𝙞𝙜𝙖𝙩𝙞𝙣𝙜 𝙂𝙤𝙤𝙙 𝙖𝙣𝙖𝙡𝙮𝙨𝙩𝙨 𝙙𝙤 𝙣𝙤𝙩 𝙘𝙡𝙚𝙖𝙣 𝙙𝙖𝙩𝙖 𝙗𝙡𝙞𝙣𝙙𝙡𝙮. They clean it with context. CTA: When you handle missing values, what is your default approach: drop, fill, or investigate first? #Python #Pandas #DataCleaning #DataScience #Analytics

So far this week, I’ve been diving into the statistical side of data analysis, which has been especially exciting given my love for numbers. I started with data visualization, focusing on the differences between bar charts and histograms and when each should be used. I also explored pie charts and their use cases, although I’ve noticed that some experts strongly dislike them and avoid using them altogether. I’m curious to hear where you stand on that. From there, I moved into more technical visualizations like line graphs and scatter plots. While studying line graphs, I learned about trendlines and how they help reveal relationships in the data. When data points cluster closely around the trendline, it suggests a positive correlation, while points that are more spread out indicate little to no correlation. However, this is not determined by sight alone. There is a statistical measure called R-squared that quantifies the strength of the relationship. I have not studied it in depth yet, but it produces a value between 0 and 1, where values closer to 1 indicate a stronger correlation. The interpretation of this value depends on the type of data being analyzed. I also reviewed the structure of graphs, specifically the independent variable on the x-axis and the dependent variable on the y-axis. One key takeaway stood out clearly. Correlation does not imply causation. Just because two variables move together does not mean that one causes the other. That is something I will carry forward as I continue studying data analysis. There is still a long week ahead, and I am looking forward to learning more. #DataAnalysis #LearningInPublic #Python #Statistics #Data

🚀 Customer Retention Analysis Project | Data Analytics I recently completed a Customer Retention Analysis project using real-world telecom data to understand customer behavior and identify key factors driving churn. 🔍 What I did: Cleaned and preprocessed customer data using Python Performed Exploratory Data Analysis (EDA) to uncover patterns Built a churn prediction model using Random Forest Visualized key insights to support business decision-making 📊 Key Insights: Customers with month-to-month contracts show higher churn rates Customers with low tenure are more likely to leave Higher monthly charges are linked to increased churn 🛠️ Tools & Technologies: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn) Google Colab GitHub 📈 Outcome: Achieved ~80% accuracy in predicting customer churn Identified key drivers that can help improve customer retention strategies 📂 Project Link: (https://lnkd.in/gmd74_gE) This project helped me strengthen my skills in data analysis, visualization, and machine learning while working on a real-world business problem. #DataAnalytics #MachineLearning #Python #CustomerRetention #ChurnAnalysis #DataScience #EDA #PowerBI #GitHubProjects

Excited to share my first data analysis project! I worked on a Customer Churn Analysis dataset where I used Python to perform exploratory data analysis and uncover meaningful insights. From cleaning the data to visualizing patterns, this project helped me understand how data can actually tell a story. One interesting insight I found was that around 26.5% of customers have churned, and factors like contract type and tenure play a major role in customer retention . Since this is my first project, I’m really excited about it and would genuinely appreciate your feedback and suggestions. You can check out my work here: GitHub Repository:- https://lnkd.in/gtW8tkQP Looking forward to learning and improving! #DataAnalysis #Python #EDA #LearningJourney #OpenToFeedback

Excited to share my latest project: Customer Churn Analysis Dashboard! I built an end-to-end data analytics solution using Python and Power BI to analyze customer behavior and predict churn. The project focuses on identifying patterns that influence customer retention and highlighting high-risk customers for proactive decision-making. A machine learning model was used to estimate churn probability, and the results were visualized through an interactive dashboard to uncover meaningful insights. 🛠 Tools Used: Python | Machine Learning | Power BI | DAX 🔗 GitHub Project: [ https://lnkd.in/d7Cp5GDE ] This project helped me strengthen my skills in data analysis, predictive modeling, and business intelligence. #DataAnalytics #PowerBI #Python #DAX #ML

🔍 Just completed my Customer Churn Analysis project — and the insights were eye-opening. I analyzed 300+ customer records using Python, Pandas, Matplotlib, and Seaborn to uncover what drives churn and how businesses can act on it. 📊 Key Findings: • Newer customers churned significantly more onboarding matters • Monthly contract holders had a higher churn rate vs annual subscribers • Customers with more support tickets showed elevated churn risk 💡 Recommendations I derived: → Improve the onboarding experience for new customers → Incentivize customers to switch to annual plans → Proactively support high-ticket users before they leave 🛠️ Tech Stack: Python • Pandas • Matplotlib • Seaborn This project strengthened my ability to turn raw data into actionable business decisions — which is exactly what data analysis is about. 🔗 GitHub: https://lnkd.in/eMU9QEJx #DataAnalysis #Python #Pandas #D

I used to think churn was just a number… until I actually tried to break it down. I worked on a Customer Churn Analysis project where I tried to answer a simple question: why do customers leave? Instead of just plotting charts, I focused on understanding patterns: - Who is most likely to churn? - What kind of plans lead to higher churn? - Where is the business actually losing money? Some things that stood out: • Customers on month-to-month contracts leave the most • New customers (0–12 months) are at higher risk • Higher monthly charges often correlate with churn What I did: - Cleaned and prepared the dataset using Python (Pandas) - Used SQL to explore churn behavior across segments - Built an interactive dashboard using Streamlit This was the first time I felt like I wasn’t just “analyzing data", I was actually answering business questions. 🔗 Live Dashboard: https://lnkd.in/dbFpzy6M If you’ve worked on churn or retention problems, I’d love to know what patterns you found. #DataAnalytics #DataScience #SQL #Python #CustomerAnalytics #ChurnAnalysis #Streamlit #DataProjects