Analyzing Vehicle Prices: Right-Skewed Distribution

Recently, I worked on a small machine learning project on Fitness Class Attendance Prediction. The goal was to predict whether a member would attend a class or not, using a complete workflow from raw data to final model evaluation. The project included: cleaning inconsistent data formats handling missing values encoding categorical variables preparing preprocessing pipelines training and comparing multiple models I tested: KNN, Decision Tree, SVM, and Naive Bayes What I found interesting was that the “best” model depended on how performance was judged: Naive Bayes gave the best F1-score on the main split SVM gave the highest accuracy Decision Tree looked like the most stable option when the test size changed A good reminder that model selection should not depend on one metric only. Github Repo: https://lnkd.in/d8_ADgY5 Projects like this keep showing me how important it is to combine clean data, correct preprocessing, and thoughtful evaluation to reach a solid conclusion. #MachineLearning #DataAnalytics #Python #ScikitLearn #ClassificationModels #DataScienceProjects

🚀 Day 86 - Matrix Plots in Seaborn Today’s focus was on Matrix Plots — a powerful way to visualize relationships and patterns across entire datasets. 📊 Here’s what I explored: 🔹 Heatmaps Used to represent data values with colors, making it easy to spot patterns, intensity, and variations at a glance. 🔹 Correlation Heatmaps Helped me understand how variables are related to each other — whether positively, negatively, or not at all. 🔹 Triangle Correlation Heatmap A cleaner version of correlation maps that removes duplicate information and improves readability. 🔹 ColorMaps in Heatmaps Learned how different color schemes can completely change the interpretation and clarity of data. 🔹 Adding Frames to Heatmaps Enhanced visualization by improving separation and making insights more structured and readable. 💡 Key Takeaway: Matrix plots are extremely useful when working with large datasets, helping to quickly identify hidden patterns, correlations, and clusters that might not be obvious otherwise. Step by step, getting closer to mastering data visualization! 🚀 #DataAnalytics #Python #DataVisualization #Heatmap #Correlation #Seaborn #MachineLearning

Revisiting Multiple Linear Regression – My ML Learning Journey As part of my ongoing machine learning journey, I revisited Multiple Linear Regression using a car dataset to strengthen my fundamentals and deepen my understanding. 🔍 What I focused on this time: • Practicing exploratory data analysis and understanding feature relationships • Visualizing how variables like HP, VOL, SP, and WT impact MPG • Building multiple models with different feature combinations • Evaluating performance using RMSE and R² score 📊 What I observed: As I added more relevant features, the model performance improved — giving a clearer picture of how multiple factors influence fuel efficiency. 💡 Why this revision mattered: Reworking the same concept helped me move beyond just “knowing” regression to actually understanding how feature selection impacts model performance. 🛠️ Tech Stack: Python | Pandas | NumPy | Matplotlib | Scikit-learn Still learning, still improving — one concept at a time. #MachineLearning #DataScience #Python #Regression #LearningJourney #DataAnalytics

📊 Recently explored 𝘆𝗱𝗮𝘁𝗮-𝗽𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 pandas library for Exploratory Data Analysis (EDA) and it’s a game changer! It provides a complete summary of the dataset with powerful visualizations, helping to quickly understand: 1️⃣ Dataset overview (structure, types) 2️⃣ Missing values detection 3️⃣ Distribution analysis 4️⃣ Correlation insights 5️⃣ Automatic visual reports 💡 One key takeaway: Before starting any data project, it’s highly valuable to review your dataset at least once using this report by ydata-profiling pandas library. It saves time, highlights hidden patterns, and improves decision-making. 🚀 Turning raw data into insights becomes much more efficient! #DataScience #EDA #Python #DataAnalysis #MachineLearning #LearningJourney

Linear Regression — Learning by Doing Took a deep dive into Linear Regression through hands-on implementation — from plotting data points to building models and visualizing predictions. 🔍 Explored: • Simple Linear Regression (finding patterns in data) • Multiple Linear Regression (using multiple features) • Polynomial Regression (capturing non-linear trends) • Data visualization & correlation analysis • Model evaluation using real predictions 📈 Watching a line (and curve) fit real data made the concepts much clearer. 💡 Theory explains, but practice makes it real. Github Repositor: https://lnkd.in/gXa9zEBs #MachineLearning #LinearRegression #DataScience #Python #HandsOnLearning

🚀 Day 36/70 – Random Variables Today I learned about Random Variables in Statistics 📊 A random variable represents the numerical outcome of a random process. 📌 Types of Random Variables 1️⃣ Discrete Random Variable Takes specific values Example: Number of heads in coin toss 2️⃣ Continuous Random Variable Takes any value within a range Example: Height, weight, temperature 📌 Python Example import numpy as np # Discrete random values data = np.random.randint(1, 10, 5) print("Discrete:", data) # Continuous random values data2 = np.random.random(5) print("Continuous:", data2) 📊 Why It’s Important ✔ Forms the base of probability theory ✔ Used in statistical modeling ✔ Helps in predicting outcomes ✔ Important for machine learning Today’s Learning: Random variables help convert real-world uncertainty into numbers 🔥 Day 36 completed 💪 Advancing deeper into statistics! #Day36 #Statistics #Probability #DataAnalytics #Python #LearningInPublic #FutureDataAnalyst #70DaysChallenge

45 Days ML Journey — Day 12: Support Vector Machine (SVM) Day 12 of my Machine Learning journey — diving into Support Vector Machine (SVM), a powerful algorithm used for both classification and regression tasks. Tools Used: Scikit-learn, NumPy, Pandas What is SVM? SVM is a supervised learning algorithm that finds the optimal hyperplane to separate data points of different classes with the maximum margin. Key concepts: Hyperplane : Decision boundary that separates classes Margin : Distance between the hyperplane and closest data points Support Vectors : Critical data points that define the boundary What if data is not linearly separable? SVM uses the Kernel Trick to transform data into higher dimensions where it becomes separable. Common kernels: Linear Kernel Polynomial Kernel RBF (Radial Basis Function) Kernel Why use SVM? Effective in high-dimensional spaces Works well with clear margin of separation Versatile with different kernel functions Code notebook: https://lnkd.in/gi_4TqUb Key takeaway: SVM is a robust algorithm that focuses on maximizing the margin, making it highly effective for complex classification problems. #MachineLearning #DataScience #SVM #Python #ScikitLearn #LearningInPublic #MLJourney

I'm doing something a little different ----> I'm learning, practicing, and building all at the same time. The data came in as one messy array. Everything was a string ----> step counts, calories, mood, all jumbled together. Before I could analyze anything, I had to separate and convert each column manually: Python date, step_count, mood, calories, sleep, activity = data.T step_count = np.array(step_count, dtype='int') Took me a while to understand WHY this works. .T transposes the array ----> rows become columns, columns become rows. Suddenly extracting one feature at a time becomes simple. Lesson: half of data science is just getting the data into a shape you can actually work with. #Python #NumPy #DataCleaning #DataScience

Data Cleaning is only half the battle. Are you Engineering your features? In Step 2 of the Machine Learning pipeline, many beginners stop at data cleaning. While removing NaNs and dropping irrelevant rows is essential, the real magic happens during Feature Engineering. While working on my recent Price Prediction project, I realized that the raw data rarely tells the full story. To build a high-performing model, you have to create features that capture the "why" behind the numbers. I focused on three key areas for this preprocessing script: 📈 Moving Averages: Capturing trends over time. 📉 Volatility: Accounting for market fluctuations and risk. 🕒 Lag Features: Giving the model a "memory" of previous price points. Clean data gets you a working model. Engineered features get you a winning model. Check out the snippet of my preprocessing logic below! 👇 #MachineLearning #DataScience #Python #FeatureEngineering #PredictiveAnalytics

📊 Day 85 – Exploring Regression Plots 🚀 Today’s learning was all about understanding relationships between variables using Regression Plots. This is where data starts telling a deeper story by showing trends and patterns clearly. Here’s what I explored: 🔹 seaborn.regplot() Learned how to visualize the relationship between two variables with a regression line. It’s a simple yet powerful way to identify trends and correlations in data. 🔹 seaborn.lmplot() Took it a step further by using lmplot() to handle more complex visualizations, including grouping data with additional categorical variables. This helps compare trends across different segments. 📈 Key Takeaways: Regression plots help in understanding linear relationships. Visualizing trends makes data interpretation easier. Useful for identifying patterns, outliers, and correlations. Great foundation for predictive modeling. Every day, I’m getting more comfortable turning raw data into meaningful insights. Excited to apply these concepts in real-world projects! 💡 #Day85 #DataAnalytics #Python #Seaborn #Regression #DataVisualization