Ahmet Kocadinç’s Post

"Just wrapped up an exciting exploratory data analysis (EDA) on a smartphone dataset! 📊 . Through visualizations and in-depth analysis, I explored key features such as operating system distribution, brand preferences, fast-charging capabilities, processor performance, and camera configurations. Here are a few highlights: Fast Charging & OS Trends: Visualized preferences for fast-charging and OS distributions to understand user demand in these areas. Brand vs. Ratings & Pricing: Analyzed brand ratings and pricing trends to see what users prioritize and value most. Processor & Memory Insights: Explored the relationship between processor brands, refresh rates, and memory capacities across brands. . Data-driven insights like these are the backbone of informed decision-making, and I’m thrilled to continue honing my analytical skills. Here’s to turning data into actionable knowledge! 🚀 . . 🔗 GitHub: https://lnkd.in/gYzTrUAf . #DataScience #EDA #SmartphoneAnalytics #DataInsights #MachineLearning #Analytics #matplotlib #visualizations

📱 Excited to share my latest project: Mobile Price Classification using Machine Learning! 🚀 💡 The primary objective of this project is to empower consumers with a tool to make informed decisions while purchasing mobile phones within their budget constraints. 🛒 Additionally, the insights garnered from this study could be invaluable for manufacturers and retailers in understanding consumer preferences and market trends. 🔍 Explore the repository on Github : https://github.com/UkaDash Mentorness #Mentorness #MachineLearning #DataScience #MobilePriceClassification #ConsumerInsights #MarketTrends #DataAnalysis

Here's a new post from my blog: A Baby eInk Dashboard https://lnkd.in/gDjpxN_U

📊 DATA STORYTELLING 101: LESSON 6 - Reimagine Your Numbers Across Time, Distance & Space !!! 🚀 Numbers are like a foreign language. We see them everywhere - news articles, shopping lists, even our paystubs - but sometimes they just don't click. Fear not, because the secret code can be cracked! Here's the key: Translate numbers in forms - time, distance, space that your audience understands from their everyday life. Numbers are like tools. Here are a few examples. ⚡ Time:  Phone companies choose to communicate the benefit of their improved batteries in units of time (hours) instead of the traditional mAh. ⚡ Distance: Knowing that you need to “run 5 kms to burn off a cheeseburger”, creates a much bugger impact than “400 calories”. ⚡ Money:  10% discount might sound okay, but “saving $10 on a $100 pizza” works better. ⚡ At the iPhone launch, Steve Jobs chose to say that, “owning an iPhone is like having a 1000 songs in your pocket” instead of the mundane “it has 5 gb memory“. Remember: Don't just see the number, see the story it tells. So next time you come across a confusing number, take a moment to reframe it into something familiar. You might just be surprised at how much clearer things become! #dataanalytics #analytics #datascience #analyst #storytellingwithdata

Diminishing Returns is my favorite thing to talk about. Where does it stop? Going from 0 to 1 will always be the largest increase. A cinema camera (7500k) to the right person can make a 100Million dollar movie. An iPhone to the right person can make a $200k revenue youtube video. Should you buy a cinema camera? Iphone return is a higher multiplier, but they are different games.

After 6 months of continuous work and 8 completed courses in Data Analytics to earn my Google Data Analytics Professional Certificate, I am now finally able to showcase my Capstone Project 🤩 – a case study about Bellabeat, a wellness company founded by Urška Sršen and Sandro Mur in 2014. It is best known for its Leaf smart jewelry wearable line. I have been asked to focus on one of Bellabeat’s products and analyze smart device data (FitBit) to gain insight into how consumers are using their smart devices. The insights discovered were then used to help guide marketing recommendations for their Marketing Analytics Team. As part of the assignment, I chose to use the programming language R to clean, prepare, process, analyze and visualize the data. Click on the link below to see the full project on Kaggle and make sure to scroll all the way down to not miss out on the key findings and recommended marketing initiatives that would satisfy the business task 😉 : https://bit.ly/3RfbkJz #dataanalytics #dataanalyst #womenindata #coding #marketinganalytics #marketinganalyst #programming #programmingjourney #google #r #rstudio #datavisualization

Can engineers read minds? Or, is it applied math and statistics through implementation of Personalized Search and Recommendation Engine? Or maybe it is enough to know that if you have an e-commerce it will increase your basket size, sales and definitely improve customer experience. Matt recently shared his thoughts on how our tailored solutions are reshaping the e-commerce terrain. It's not about reading minds, but rather about understanding behaviors and preferences to deliver precisely what customers need, often before they even realize it themselves. #ML #machinelearning #ecommerce #search #recommendation

Machine Learning Task 2: Customer's of a retail store based on their purchase history by using K-means clustering algorithm #ProdigyInfoTech #MachineLearning PRODIGY_ML_02

I am pleased to share that I have successfully completed a practical exercise as part of my Data Analytics Bootcamp! This project proved to be both challenging and rewarding, and I am excited to provide an overview of the work I undertook. Objective: Conduct a comprehensive comparison analysis of iPhone 15 sales vs. iPhone 14 sales. Datasets Utilized: fact_sales_Iphone14 fact_sales_Iphone15 dim_stores Overview: Data Review: Conducted a thorough analysis of the sales data for both iPhone 14 and iPhone 15. Metric Comparison: Compared key sales metrics, including units sold and revenue, across both iPhone models. Variance Calculation: Absolute Difference: Calculated the absolute sales difference between iPhone 15 and iPhone 14. Percentage Variance: Determined the percentage variance in sales between the two models. Reporting: Compiled a comprehensive report that highlights the differences in sales performance across the top 10 countries. This exercise has significantly strengthened my data analysis skills and provided deeper insights into market trends and sales performance. I’ve attached a screenshot showcasing a snapshot of my solution. A special thank you to my mentors, Hemanand Vadivel and Dhaval Patel from Codebasics, for their invaluable guidance throughout this project. #dataanalytics #dataanalyst #codebasics #learning #cfbr #linkedin #data #skills #powerbi #dashboard #charts #bar #slicer #graph #columnchart

Excited to share my latest project on Kaggle - a predictive analysis on credit card customer churn! 💳📊 In this endeavor, I delved deep into the dataset to uncover insights that can revolutionize customer retention strategies for financial institutions. Here's a snapshot of what I did: ✅Exploring the Data: I started by thoroughly exploring the dataset, cleaning the data, and visualizing key trends to understand customer behavior. ✅Insights and Recommendations: Drawing insights from the analysis, I provided actionable recommendations for financial institutions to mitigate customer churn. ✅Feature Engineering: Next, I engineered relevant features to enhance the predictive power of the model, transforming raw data into meaningful indicators. ✅Model Development: I used Naive Bayes Classifier for its unique probabilistic approach By training and evaluating the model, the accuracy score is 87.86% ✅Performance Evaluation: I rigorously evaluated the models' performance using metrics such as accuracy, precision, recall, and F1-score to ensure their effectiveness in real-world applications. You can explore the detailed notebook on Kaggle: https://lnkd.in/dsWKJNCR on Github:  https://lnkd.in/dvVyBuKA #DataScience #MachineLearning #PredictiveAnalytics #CustomerRetention 🚀🔍