Predicting House Prices with Linear Regression and Feature Engineering

🚢 Titanic Survival Prediction – Exploratory Data Analysis (EDA) Excited to share my recent project where I performed in-depth EDA on the Titanic dataset to uncover key factors influencing passenger survival. 📊 Key insights: Gender and class had a major impact on survival rates Age and fare revealed interesting patterns Data visualization helped uncover hidden trends 🛠 Tools used: Python, Pandas, NumPy, Matplotlib, Seaborn This project strengthened my skills in data analysis, visualization, and storytelling with data. #DataAnalytics #EDA #Python #DataScience #MachineLearning #StudentProject #LearningJourney

Iris Classification using K-Nearest Neighbors (KNN)🌸 Excited to share my Machine Learning project where I implemented the K-Nearest Neighbors (KNN) algorithm to classify iris flower species based on their features such as sepal length, sepal width, petal length, and petal width. This project helped me understand how distance-based algorithms work for classification problems and how to evaluate model performance using standard metrics. 🔍 Key Highlights: • Data Exploration and Visualization • Implementation of K-Nearest Neighbors (KNN) Algorithm • Feature Scaling and Model Training • Model Evaluation using Accuracy and Confusion Matrix • Classification of Iris Flower Species 🛠 Technologies Used: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • Jupyter Notebook 🔗 GitHub Repository: https://lnkd.in/dVn-5C9Y This project strengthened my understanding of supervised learning, classification algorithms, and model evaluation techniques in Machine Learning. #MachineLearning #KNN #DataScience #Python #Classification #ArtificialIntelligence #MLProject #LearningJourney

Project: House Price Prediction using #DecisionTreeRegressor Excited to share my recent project where I built a House Price Prediction model using a Decision Tree Regressor. - Key Highlights: Performed data preprocessing and handled categorical features Built a regression model to predict house prices based on multiple factors Achieved: -R² Score: 0.95 -MAE: 35,453 Insights: The model effectively captured non-linear relationships in the dataset Gained practical understanding of decision tree working and hyperparameters Learned how to control overfitting and improve model performance Tech Stack: Python | Pandas | NumPy | Scikit-learn | Matplotlib This project helped me strengthen my understanding of regression techniques and real-world data handling. Next step: Improving performance using ensemble techniques like Random Forest. - Grateful for the guidance from Abhishek Jivrakh Sir during this Project. GitHub Link : [ https://lnkd.in/g8qw8NMF ] #MachineLearning #DataScience #Python #AI #DecisionTree #Regression #Projects #Learning #StudentDeveloper

Merging Industrial Chemistry with Data Science 🧪💻 I’ve been working on automating laboratory data analysis using Python. In this project, I used Pandas for data cleaning (handling missing pH values via interpolation) and Matplotlib to visualize a titration curve. By calculating the first derivative ({dpH / dVol}), I was able to mathematically determine the exact equivalence point at 10.1 mL. This is part of my journey to build digital tools that streamline chemical analysis and industrial workflows. https://lnkd.in/ec7qSz_v #DataScience #IndustrialChemistry #Python #Pandas #ChemistryTech #BuildInPublic

🔭 We explored classic experiments by Michelson and Newcomb measuring the speed of light, applying modern data analysis techniques to quantify their findings. It's incredible to see how statistical methods, like bootstrapping, allow us to estimate fundamental constants and understand the uncertainty in experimental measurements. We tackled challenges like data transformation and outlier detection, proving that robust data science skills are essential, even when looking back at groundbreaking scientific history. This project highlights the power of Python (NumPy, Pandas, Matplotlib) in bringing historical scientific data to life and extracting valuable insights. What other historical datasets do you think would benefit from a fresh data science perspective? DataScience #Physics #SpeedOfLight #DataAnalysis #Statistics #Python #NumPy #Pandas #Matplotlib #ScientificResearch #HistoricalData

📊 Exploring Data with the Iris DatasetRecently, I worked on a simple yet insightful data visualization task using the famous Iris dataset. This exercise helped me strengthen my understanding of data analysis fundamentals. 🔹 Loaded and explored the dataset using pandas 🔹 Analyzed structure with shape, columns, and summary statistics 🔹 Created visualizations using matplotlib & seaborn: ✔️ Scatter plot to study relationships ✔️ Histogram to understand distribution ✔️ Box plot to identify outliers This task enhanced my skills in data exploration and visualization, which are essential for any data science workflow. #DataScience #Python #DataVisualization #Pandas #Seaborn #Matplotlib #MachineLearning #LearningJourney  DevelopersHub Corporation©  

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

Completed my Pandas assignment today — and honestly, it was a great learning session! 📊 Worked with two real-world datasets — the Iris Flower dataset and the Titanic dataset — and applied a range of data analysis operations using Python & Pandas. Here's what I explored: 🌸 Iris Dataset • Displayed the first 10 rows, shape, data types & summary statistics (mean, std, min, max) • Filtered rows where petal_length > 4.5 and species = "Iris-virginica" • Grouped by species to compute average sepal_length, max petal_width & std deviation of sepal_width • Created a new column "petal_ratio" = petal_length / petal_width and found the average per species 🚢 Titanic Dataset • Selected specific columns: Name, Sex, Age, Fare, Survived • Filtered female passengers with Fare > 30 • Grouped by Pclass and computed: survival rate, average fare & average age → 1st class: ~63% survival | 2nd class: ~47% | 3rd class: ~24%

🚀 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

🚀 Recently I’ve been diving deeper into the world of Data Science & Machine Learning! I’ve explored some powerful Python libraries that are essential for data analysis and visualization: 🔹 NumPy – for numerical computing 🔹 Pandas – for data manipulation & analysis 🔹 Matplotlib – for data visualization 🔹 Seaborn – for advanced and attractive visualizations Step by step, I’m building a strong foundation in ML and continuously improving my problem-solving skills. 📌 Check out my learning progress and resources here: https://lnkd.in/gUHRnfwP #MachineLearning #DataScience #Python #NumPy #Pandas #Matplotlib #Seaborn #LearningJourney #CSE