I recently worked on a small machine learning project where I tried predicting housing prices using Decision Tree Regression. I used the California Housing dataset and went through the full process — cleaning the data, exploring patterns, building the model, and evaluating how well it performs. It was interesting to see how different factors like income and location influence house prices, and how decision trees handle these relationships. This project gave me a better understanding of how regression models work in practice and the importance of avoiding overfitting while tuning the model. 🔗 Link:- https://lnkd.in/gzwVU_dn #MachineLearning #DataScience #Python #LearningJourney

📌 Categorical Plots in Seaborn – Swarm Plot A swarm plot in Seaborn is used to display individual data points across categories without overlapping. It is created using sns.swarmplot() and arranges the points in a way that avoids overlap, making the distribution of data easier to see. Swarm plots help visualize how values are spread within each category while clearly showing every observation. They are commonly used in exploratory data analysis to understand the distribution and density of categorical data. #Python #Seaborn #DataVisualization #DataAnalytics #LearningPython

📌 Grid Plots in Seaborn – PairGrid PairGrid in Seaborn is used to create a grid of plots for exploring relationships between multiple variables in a dataset. It allows us to map different types of plots to different parts of the grid, such as scatter plots, histograms, or regression plots. PairGrid provides more flexibility and customization compared to pairplot(), making it useful for advanced data visualization. It is commonly used in exploratory data analysis to examine relationships and patterns between multiple variables. #Python #Seaborn #DataVisualization #DataAnalytics #LearningPython

🚀 Day-70 of #100DaysOfCode 📊 NumPy Practice – Finding Top K Elements Today I worked on finding the top 3 largest elements in a NumPy array. 🔹 Concepts Practiced ✔ Array sorting using np.sort() ✔ Array slicing ✔ Extracting top values from datasets 🔹 Key Learning Finding top-K elements is a common task in data analysis, ranking systems, and machine learning, where identifying the most significant values is important. Step by step improving my NumPy and data manipulation skills 🚀 #Python #NumPy #DataScience #PythonProgramming #100DaysOfCode #LearningJourney

In this project, I analyzed real-world car sales data and built predictive models to estimate selling prices based on features like mileage, brand, year, and condition. 🛠 Tools Used: Python, Pandas, NumPy, Scikit-learn, Matplotlib 📊 Models: Linear Regression, Random Forest, Gradient Boosting 📈 Evaluation Metrics: R² Score, MAE, RMSE This project strengthened my skills in data preprocessing, EDA, and predictive modeling. #DataScience #MachineLearning #Python #StudentProject #DataAnalytics

🚢 Titanic Survival Analysis Project Analyzed the Titanic dataset to explore patterns influencing passenger survival using Python and exploratory data analysis. 🔎 Identified key factors such as gender, passenger class, and age that significantly impacted survival rates. 📊 Performed data cleaning, preprocessing, and visualization to uncover meaningful insights. 📌 Compared survival patterns across different passenger groups to better understand historical outcomes. 🛠 Tools: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook 🔗 GitHub Repository: https://lnkd.in/gXBzREJ7 #DataAnalytics #DataScience #Python #EDA #MachineLearning

Successfully developed and executed a Machine Learning program on the California Housing Dataset 🏠📊 ✔️ Created histograms for all numerical features to analyze data distribution ✔️ Generated box plots to identify potential outliers ✔️ Performed detailed feature distribution analysis Strong foundation in Exploratory Data Analysis (EDA) before model building! 🚀 #MachineLearning #DataScience #EDA #Python #AIStudent

🚀 Machine Learning Project: Housing Price Prediction I recently built a Linear Regression model to predict house prices based on features such as area and number of bedrooms. 🔹 Tools Used: Python, Pandas, NumPy, Matplotlib, Scikit-learn 🔹 Steps: • Data preprocessing • Train-test split • Linear Regression model training • Model evaluation 📊 Visualized the relationship between house area and price using regression plots. This project helped me strengthen my understanding of regression models and data preprocessing. 🔗 GitHub: https://lnkd.in/dSe2YRzY Colob link :-- https://lnkd.in/ds52b_YY #DataScience #MachineLearning #Python #LinearRegression

📊 Today I learned ANOVA & Tukey Post-Hoc Test in Statistics. Key concepts I covered: • ANOVA → Used to compare 3 or more groups • Between-group vs Within-group variation • F-statistic = Signal / Noise • Post-hoc Tukey test → Identify which groups differ • SSW → Sum of Squares Within • MSW → Mean Square Within • HSD → Minimum difference needed for significance Learning statistics step by step for data analysis and machine learning. #Statistics #DataAnalytics #ANOVA #DataScience #LearningJourney #Python #MachineLearning #LinkedInLearning #DataAnalysis

🐍 Day 80 — Sampling and Population Day 80 of #python365ai 🧪 Population → entire dataset Sample → subset of data 📌 Why this matters: We usually analyse samples to infer properties of a population. 📘 Practice task: Take a small sample from a dataset and compute its mean. #python365ai #Sampling #Statistics #Python