Building End-to-End Car Price Predictor from Scratch

🔷A simple train test split is not always enough. I learned this the hard way when my model looked great on paper and struggled on real data. 📌Here is what nobody tells you about splitting data properly. The basic split gives you two sets. Training and testing. That works for simple projects. But what if you need to tune your model? You test different settings, pick the best one, and evaluate on the test set. The problem is that you have now indirectly used the test set to make decisions. It is no longer a fair judge. This is where a three way split becomes important. 🔹X_train, X_temp, y_train, y_temp = train_test_split( X, y, test_size=0.3, random_state=42 ) 🔹X_val, X_test, y_val, y_test = train_test_split( X_temp, y_temp, test_size=0.5, random_state=42 ) Now you have three sets. Training set. The model learns here. 70 percent of your data. Validation set. You tune and compare models here. 15 percent. Test set. You evaluate the final model here. Once. Never again. 15 percent. The test set is sacred. You look at it exactly one time at the very end. One more thing that most people miss. Always stratify your split when your target column is imbalanced. 🔹train_test_split(X, y, stratify=y, test_size=0.2) stratify=y makes sure both sets have the same proportion of each class. Without it you might end up with a training set that barely sees the minority class and a model that has no idea it exists. The split is not a formality. It is a decision that shapes every result that follows. Get it right before you touch anything else. ❓What split ratio do you use for your projects and why? #DataScience #MachineLearning #Python

Week 1 of building SmartOps in public. SmartOps is an AI-powered customer follow-up tool The problem I am solving: A food seller has 20 customers. 11 of them silently stopped ordering last month. She has no idea. That is lost revenue she will never recover because nobody told her to call them. This week I built the first piece, a Python script that: - Tracks all customers in one place - Calculates how long each person has been inactive - Generates a daily "call these people today" list automatically Next week: cleaning real messy business data with Pandas. This is Week 1 of 12. Every week I will share what I built, what I struggled with and what I learned. Built with Python + Pandas. Synthetic data used for demonstration. Full writeup on Medium: [https://lnkd.in/ekQBT5Zf] GitHub: [https://lnkd.in/eD4suFWY] Here is what the output looks like #BuildingInPublic #AIML #SmartOps #NigerianTech

sat down and started working on a real dataset (Bank Marketing), and honestly… it felt a mix of confusing and exciting at the same time. Here’s what I managed to do: • Loaded the dataset using Pandas• Tried to understand what the data actually looks like• Checked data types (realized not everything is numbers 😅)• Looked for missing values — and found that some are hidden as “unknown”• Ran summary statistics to understand the data better• Tried creating visualizations using Seaborn and Matplotlib• Got errors… fixed them… learned from them (this was the real learning moment) 💡 One thing I understood today:Data is not clean and ready — you have to explore, question, and fix things before doing any real analysis. It wasn’t perfect, but it was a start.And I’m showing up again tomorrow. #LearningInPublic #DataAnalyticsJourney #Python #BeginnerJourney #coding ninjas #skillefied mentor

Ever opened a dataset and thought… “why is this so messy?” 😅 Same here. While working with Pandas, I realized data cleaning isn’t complicated — it’s just a few powerful steps repeated smartly 👇 🧹 Missing values? → isna() to find them, fillna() or dropna() to handle them 🔁 Duplicate rows? → drop_duplicates() and move on 🔧 Wrong data types breaking your logic? → astype() fixes it in seconds 🧼 Messy text (extra spaces, weird formats)? → str.strip() and str.lower() clean it instantly 📊 Before trusting data? → info() and value_counts() give a quick reality check Good analysis starts with clean data first. That simple shift has already changed how I look at datasets. Still learning, but this is one of the most useful lessons so far. #DataAnalytics #Python #Pandas #DataCleaning #LearningJourney

One of the most common sources of confusion for pandas beginners and even experienced analysts is knowing when to use apply(), map(), and applymap(). They look similar. They sometimes produce the same result. But they are designed for completely different situations. map() is for single column transformations and value substitution. apply() is for complex row-level or column-level logic across a DataFrame. DataFrame.map() is for applying the same transformation to every individual cell. And before reaching for any of them — always check if a vectorized operation can do the job faster. Getting this right means cleaner code, better performance, and fewer bugs in your data pipelines. Read the full post here: https://lnkd.in/e8sJfEgh #Python #Pandas #DataScience #DataEngineering #DataAnalysis #Analytics

Hey everyone 👋 I recently built a small project that I’m really excited about — a CSV AI Agent 📊🤖 Github Repo: https://lnkd.in/djDbQJ5z Live Demo: https://lnkd.in/ddJTzTw2 The idea was simple: What if you could just talk to your data instead of writing code? 🔍 Analyzing Data 📊 Visualizing Insights 🤖 AI-Powered Responses ⚡ Instant Results You can upload any CSV file and ask questions in simple English like: 👉 “What’s the average sales?” 👉 “Show top 10 categories” And it gives you answers + creates charts automatically! 💻 Built with: Python, Streamlit, LangChain, Groq (Llama 3.3), Pandas, Matplotlib & Seaborn 🔐 Note: To try the app from my link, you’ll need your own Groq API key — just plug it into the sidebar and you’re good to go! Still improving this project—would love your feedback and suggestions 😊 #AI #DataScience #Python #Streamlit #LangChain #Groq #MachineLearning #DataAnalytics #BuildInPublic #LearningJourney #TechProjects #AIProjects

Stop using Pandas for everything. I just published a full breakdown of 7 Python libraries that are redefining how developers build in 2026 with install commands + real code examples for each. Here's the quick cheat sheet: ⚡ Polars → 10x faster than Pandas for big data 📄 MarkItDown → Converts PDFs/Word docs into AI-ready Markdown 🤖 Smolagents → Build your first AI agent in 10 lines 🧑✈️ GPT Pilot → An AI that writes entire features, not just autocomplete 📱 Flet → Build web + mobile + desktop apps in pure Python 🛡️ Pyrefly → Catch bugs BEFORE you run your code (Meta-built) 🌐 Morphik-Core → AI that understands images and videos, not just text You don't need to learn all 7 today. Pick the one that solves YOUR problem right now. Working with data? → Polars Building an app? → Flet Curious about agents? → Smolagents The full blog (with code examples for every library) is linked in the comments 👇 Which of these are you already using? Drop it below 🔽 #Python #AI #MachineLearning #Programming #Developer #TechIn2026 #AITools #OpenSource #PythonDeveloper #CodingTips

Pandas is about to get replaced. Not tomorrow. But in 2 years, half of you will have switched to Polars. And the other half will be wondering why their scripts are still slow. Polars is: → 5-30x faster than Pandas (on real benchmarks) → Memory-efficient (no more OOM errors on 10GB datasets) → Written in Rust (lazy evaluation, query optimization built in) → Has a cleaner, more consistent API than Pandas → Native support for streaming data (no chunking required) My free notebook walks through the fundamentals: → Polars DataFrames — creation, inspection, indexing → The expressions API (the thing that makes Polars fast) → Filtering, selecting, sorting — the Pandas equivalents → group_by with expressions (way cleaner than agg) → Lazy evaluation — query optimizer explained → Side-by-side Pandas vs Polars benchmarks If you've never heard of Polars, you're about to. Get ahead of the curve. https://lnkd.in/gDXKkV75 Day 2/7. #Polars #Python #DataEngineering #DataAnalytics #Pandas #Rust #DataFrames #OpenSource

Data View v1 is live. No hype — just a clean build. Built with Streamlit, Python, Pandas, NumPy, Seaborn, and Matplotlib, this app cuts through the noise and gets straight to the point: understanding your data without wasting time. What it handles right now: • Upload your dataset • Quick data overview • Basic cleaning • Statistical insights • Correlation analysis • Visuals — bar, histogram, pie It’s not flashy. It’s functional. And it works. But this is just the opening move. Now your move 👇 • What’s one feature you’d add next? • What would make you actually use this daily? • What’s missing? Be direct. I’m listening. I’ll be shipping a sharper version every Monday — better features, tighter experience, smarter analysis. No excuses, just iterations. Because good products aren’t guessed — they’re built, tested, and refined. live demo --> https://lnkd.in/gXda-aZs #BuildInPublic #DataScience #Streamlit #Python #KeepBuilding

📊 Day 12 | Choosing the Right Test & Practical Tips 🧠📊 Today, I learned how to choose the right statistical test based on the data and problem. After exploring multiple statistical tests, I realized that the most important skill is not just knowing tests, but knowing when to use which test. The selection depends on: 🔹 Type of data (Numerical or Categorical) 🔹 Number of groups (1, 2, or more) 🔹 Relationship between data (independent or dependent) Some simple rules I learned: ✔ One group vs value → One-sample t-test ✔ Two independent groups → Two-sample t-test ✔ Same group (before/after) → Paired t-test ✔ More than two groups → ANOVA ✔ Categorical data → Chi-Square test I also learned some common mistakes: ❌ Relying only on p-value without understanding data ❌ Not checking assumptions like normality ❌ Misinterpreting results To understand this better, I applied multiple tests on a dataset using Python 💻 This helped me see how different tests are used in different scenarios. Instead of guessing, we can now select the right test and make data-driven decisions 📊🚀 #Statistics #HypothesisTesting #DataScience #DataAnalytics #LearningInPublic #Python