Tamer Ahmed’s Post

🚀 Excited to Share My Recent Project: Predicting Ad Clicks with Logistic Regression! 📊 Project Overview: I recently had the opportunity to dive into the fascinating world of data science and machine learning. 🤖💡 My focus? Building a robust logistic regression model to predict whether users would click on advertisements displayed on a company website. 🌐 📈 The Dataset: I worked with a synthetic advertising dataset (yes, it’s fake, but the insights are real!). It contained various features related to user behavior, such as age, time spent on the site, gender and so on. 📱💻 🔍 Objective: The ultimate goal was to create a model that could accurately predict whether a user would click on an ad. Why is this important? Well, companies invest significant resources in online advertising, and understanding user behavior helps optimize ad campaigns. 💰🎯 🔧 Approach: *Data Exploration: I started by exploring the dataset, checking for missing values, and understanding the distribution of features. *Feature Engineering: I engineered relevant features. *Model Building: Logistic regression was my weapon of choice. It’s elegant, interpretable, and perfect for binary classification tasks. Model Evaluation: I split the data into training and testing sets, trained the model, and evaluated its performance using metrics like accuracy, precision, and recall. 📊 Results: My logistic regression model achieved an impressive accuracy of 88% on the test set! 🎉 It correctly predicted whether a user would click on an ad in most cases. 🔗 GitHub Repo: Curious to see the code? Check out my GitHub repository where I’ve shared the Jupyter Notebook and all the juicy details: https://lnkd.in/e4AaZpu5 👉 Connect with me! If you’re as passionate about data science as I am, let’s connect! Feel free to drop a comment or send me a message. 🤝 #DataScience #MachineLearning #LogisticRegression #AdClickPrediction

TASK-3 ✅ Predict customer purchase behavior with a decision tree classifier! Explore my latest project using demographic and behavioral data for actionable insights. #DataScience #MachineLearning #CustomerAnalytics #PredictiveModeling #Python #DataAnalysis #Progidy https://lnkd.in/gCFJdipT

I've learned how to apply pd.cut function to create new features on the data and hypothesis testing, along with many more techniques to get meaningful insights from data

If you're on the hunt for #alpha 💸 in your #optionstrading strategy or platform, this blog has a few good tips for you. [Hint - 🤫 you're going to want to check out our #unusualoptions data feed!] https://hubs.ly/Q02DKwDK0

The goal of this case study is to demonstrate how we can use Japio to transform the data and use it wherever needed, maybe the transformed data would be used to train a model or use in an external BI tool like PowerBI, Tableau. After transforming the data in Japio we can also visualize it in the form of various metrics and dashboards. This is all you can do without a coding skill using the Japio platform. In the below section we will compare one of the kaggle articles where user had to use coding skills (python) to analyze, clean & transform the data. In the later section we will demonstrate how you can achieve the same using Japio without any coding. https://lnkd.in/gF6w3-xE

💳 How to encourage credit card users to increase usage with targeted strategies? 🐍 During the 6th-8th week of RevoU's FSDA Program, I learned how to use Python as one of the data analytics program languages and apply it to this project. Several libraries like Pandas, Numpy, and Seaborn were used during the process. 🔍 The project's main goal is segmenting customers using K-Means to understand RevoBank's user characteristics and tailor targeted strategies for each segment. 🏆 For the highlight, based on their sales, frequency, recency, and with the consideration of cluster numbers, 3 clusters were generated. The top cluster is users with high spending and high frequency. Therefore, investing in this cluster will ensure their engagement and high spending. In this case study, you will find: 🧼 Data cleaning and preparation 🔍 Data exploration and insights 📊 Customer segmentation 📈 Recommendations I remember those 3 weeks were really challenging, but thanks to Kak Muhamad Mustain🙌, the basics and concepts were very well-explained, making it easier for me to understand and complete the project. 🚀 You can swipe through my project in this post or go to https://lnkd.in/g3wsmDiC for more interactive deck.

This tutorial shows you exactly how to analyze and leverage #UnusualOptions 📊 data. It's a powerful way to track unique shifts in the market and predict what's next! https://hubs.ly/Q02KS5sH0

👌 Looking to Enhance Your Data Quality? This is for You 🖋️ Author: Rahul Madhani 🔗 Read the article here: https://lnkd.in/e4BQfyvr ------------------------------------------- ✅ Follow Data Engineer Things for more insights and updates. 💬 Hit the 'Like' button if you enjoyed the article. ------------------------------------------- #dataengineering #python #datascience #data

# Data Cleaning: Tame the Jungle with Flookup Data Wrangler Today, we are going to talk about the delightful chaos that is data cleaning. If you are like most in this field, then you have probably spent countless hours wrestling with Pandas and Python, trying to whip your datasets into shape. But fear not, because there's a new(ish) kid on the block — Flookup! And trust me, it's about to make your life a whole lot easier. ## The Pandas Predicament 🐼 Let's be real for a moment. Pandas is powerful, but it's also like trying to teach a cat to dance ballet. It's possible, but you'll end up with more scratches than applause. Here's the thing: Pandas requires you to remember a ton of commands, syntax quirks, and data structures. One wrong move and you're stuck debugging for hours. Fun, right? Not so much. ## Python, the Slippery Serpent 🐍 And Python? Oh boy, don't get me started. Python is great for a lot of things, but it's also like that one friend who insists on using 20 different ingredients to make a sandwich. Sure, it's fancy, but sometimes you just want a simple PB&J without the hassle of artisanal bread and organic peanuts. ## Enter Flookup: The Animal Tamer (or should I say... Wrangler?) 🕸️ Now, picture this: You have a tool that cleans your data with incredible ease and efficiency. That's Flookup for you. It’s like having a trusted assistant for your spreadsheets. Here's why Flookup beats Pandas and Python hands down: + Simplicity: No more deciphering cryptic code. Flookup is intuitive and user-friendly. If you can type, you can use Flookup. + Speed: Forget about running complex scripts that take ages. Flookup gets the job done in seconds. It's like data cleaning on rocket fuel. + No Code, No Cry: Let's face it — coding isn't everyone's cup of tea. With Flookup, you can achieve the same results without writing a single line of code. + Accuracy: Flookup handles fuzzy matching like a pro. Say goodbye to those pesky false positives and hello to pristine data. ## The Final Verdict Wild animals are called "wild" for a reason and data cleaning does not have to feel like a trip through the jungle. Do not torture yourself with the complexities of Pandas and Python when you can embrace the #NoCode simplicity and power of Flookup: https://www.getflookup.com #Data #DataCleaning #DataScience

I explain how to fit a gradient boosting model and use your model to score new data. In Part 6, Part 7, and Part 9 of this series, we fit a logistic regression, decision tree and random forest model to the Home Equity data we saved in Part 4. #SASUsers