Manish Kumar mahto’s Post

🚀 Day 122 in my data science journey! Today's highlights: 🔍 Understanding Covariance: Interpretation and calculation. ⚠️ Disadvantages: Limitations explored. 🔄 Covariance with Itself: Demystified! 🔍 Purpose of Correlation: Solving relationship problems. 📈 Exploring Correlation: Definition and differences from covariance. 🔍⚖️ Correlation vs. Causation: Important distinction! 📊 Visualizing Multiple Variables: Insights unlocked! Excited for more discoveries! 💡 #DataScience #LearningJourney 📚

Excited to dive into my first data science project: classifying Iris flower species using machine learning! 🌸📊 Exploring data analysis, visualization, and predictive modeling to understand this classic dataset. #DataScience #IrisDataset #MachineLearning #DataVisualization #Codsoft Task 3:Iris Flower Classification

Exploring Titanic Predictions: Insights from Data Science Project Highlights: Data Exploration: Delved into the dataset to understand key variables influencing survival rates. Model Development: Implemented and compared various algorithms, including logistic regression and decision trees. Feature Engineering: Created new features from existing data to improve model accuracy. Visualization: Designed clear and informative visualizations to convey findings and model performance. GitHub: https://lnkd.in/dxyyEuaD Data from Kaggle: https://lnkd.in/dhgDXH4s #DataScience #MachineLearning #PredictiveAnalytics #Titanic #DataVisualization #FeatureEngineering

TASK 2: IRIS Flower Classification🌸 I'm excited to share my recent project on classifying the IRIS flower dataset using data science techniques and the KMeans clustering algorithm! 🌼 #CodSoft #DataScience

Day 221 of My Data Science Journey 🚀 Today, I delved into the fascinating world of Feature Selection. 🌟 - Why Feature Selection? 🤔 - Types of Feature Selection 🛠️ - Filter-Based Feature Selection 📊 - Variance Threshold Method 🎯 - Correlation 🔗 - ANOVA 📈 - Chi-Square 🎲 Each of these concepts helps in refining the model by selecting the most relevant features, making the model both efficient and accurate! 💪 Excited to put this into practice and see the impact on my models. Onward and upward! 🚀 #DataScience #MachineLearning #FeatureSelection #LearningJourney #DataScienceJourney

#Day_14 #Seaborn We Moving Forward our data Science Jounrey. We are learn today one of the most data visulaization Library is Seaborn #Why-provides a layer of abstraction hence simpler to use. better aesthetics. more graphs included. #Note - Seaborn is all graph is divide into the two part 1- #axis label graph 2 - #figure label graph #Main_Classification Relational Plot Distribution Plot Categorical Plot Regression Plot Matrix Plot Multiplots

day 120 on data science journey - 📊 Univariate Analysis: - Explored Frequency Distribution Table & Cumulative Frequency 📈 for categorical data. - Analyzed Histograms for numerical data. - Bivariate Analysis: - Investigated Categorical - Categorical (Contingency Table/Crosstab) 🔄. - Explored Numerical - Numerical (Scatter plot) relationships. - Examined Categorical - Numerical analysis. 💡 Ready to apply these skills in real-world scenarios! #DataScience #Mathematics #Analysis

My first data science portfolio project at dibimbing.id! Ever wondered what factors influenced the survival of Titanic passengers? I tried to answer that question by building a predictive model using data science. The results were quite interesting! Check out my portfolio for more details. #datascience #titanic #machinelearnin  #Dibimbing #DigitalSkillFair31g #datavisualization

Excited to introduce DataStalgia! As part of my journey into Computer Vision, I’ve decided to revisit the major areas of Data Science that I’ve already mastered—an exciting "nostalgia trip" to reinforce foundational concepts before exploring new horizons. Throughout this journey, I’ll cover key topics in data science, sharing insights and reflections along the way. Join me on this DataStalgia adventure! #DataScience #DataStalgia #LearningJourney #ComputerVision #Growth

#Day81 #100DaysOfDataScience Day 81 of my Data Science adventure! 📚 Today, I dove deep into the world of Gaussian curves and explored the beauty of positive and negative skewness in data distributions. 🌟 The Gaussian curve, or the bell curve, is a way of visualizing data that follows a normal distribution.  This curve is symmetrical, with most of the data clustering around the mean. Positive skewness means the data tails off to the right, while negative skewness means it tails off to the left.  Understanding these patterns is crucial for making sense of real-world data! 📊 #DataScience #GaussianCurve #Statistics #PositiveSkew #NegativeSkew #DataVisualization #LearningEveryday Follow for more insights and keep riding the wave of knowledge! 🌊🚀