Titanic Survival Prediction with Decision Tree Classifier

🏆Excited to share my latest work on Machine Learning & Al Practicals! I've created a collection of hands-on Jupyter Notebooks covering core ML concepts and algorithms as part of my academic learning journey. This project helped me strengthen my understanding by implementing models from scratch and analyzing real datasets. Key topics covered: DataFrame Operations Correlation Matrix Normal Distribution Simple Linear Regression Logistic Regression Decision Trees (ID3 Algorithm) Confusion Matrix Decision Tree Pruning Tools & Technologies: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook Through this project, I gained practical experience in: Data preprocessing Model building & evaluation Data visualization Understanding ML algorithms in depth Check out my GitHub repository: https://lnkd.in/gJCenmxd I'm continuously learning and exploring more in the field of AI & ML. Open to feedback and suggestions! #Machine Learning #ArtificialIntelligence #DataScience #Python #LearningJourney #GitHub #Students #AI #ML

Excited to share my latest project on Bayesian Linear Regression, where I explored how probabilistic modeling can be used not only to generate predictions, but also to quantify uncertainty with more rigor than traditional regression approaches.  This project helped deepen my understanding of statistical modeling, machine learning fundamentals, and data-driven decision-making with mathematical concepts behind the code. It was really satisfying when I started with derivations first followed by the code. The github repository with mathematical derivations included is here https://shorturl.at/41yz2  #MachineLearning #DataScience #AI #BayesianStatistics #Python #StatisticalModeling #Analytics

🚢 Titanic Survival Prediction using Logistic Regression | Machine Learning Project I developed a Machine Learning model to predict whether a passenger survived the Titanic disaster using Logistic Regression. 🔹 Key Steps: Performed data cleaning and preprocessing Handled missing values (Age, Embarked) Removed unnecessary columns (Name, Ticket, Cabin, PassengerId) Converted categorical data into numerical format Split data into training and testing sets Trained the Logistic Regression model 📊 Model Performance: Achieved ~79% accuracy Evaluated using Confusion Matrix and Classification Report 💡 Key Learnings: Understanding of classification algorithms Importance of data preprocessing Model evaluation using precision, recall, and F1-score This project gave me hands-on experience in solving a real-world classification problem using Python and Machine Learning. #MachineLearning #DataScience #Python #LogisticRegression #DataAnalysis #AI #Projects

🚀 Machine Learning in Action — Linear Regression Model Excited to share a small step in my Machine Learning journey! I recently built a Linear Regression model using Python to analyze and visualize relationships in the diabetes dataset. 📊 What this project includes: • Data preprocessing and feature selection • Training a Linear Regression model using Scikit-learn • Visualizing results with Matplotlib • Plotting the regression line to understand the relationship between variables 🔎 The visualization clearly shows how the model fits the data, helping interpret patterns and trends within the dataset. Projects like this help strengthen my understanding of machine learning fundamentals, data visualization, and model evaluation. Always learning and exploring new ways to turn data into insights. 📈 #MachineLearning #DataScience #Python #AI #LinearRegression #DataAnalytics #LearningJourney

🤖 Machine Learning Models I Recently Learned As part of my Data Science learning journey, I explored several Machine Learning algorithms. Here are some models I practiced with: ✔ Logistic Regression ✔ Decision Tree ✔ Random Forest ✔ K-Nearest Neighbors (KNN) ✔ Gradient Boosting Each model has its own strengths depending on the dataset and problem. Through practice projects, I am learning how to: • Train models • Evaluate performance • Compare model results • Choose the best algorithm Excited to continue learning and applying Machine Learning to real-world problems. #MachineLearning #Python #DataScience #LearningJourney

Completed Task 3 – Model Validation & Hyperparameter Tuning in Machine Learning As part of my learning journey, I worked on improving a regression model by analyzing overfitting and applying advanced techniques like cross-validation and hyperparameter tuning. Key Highlights: • Performed overfitting analysis using Decision Tree Regressor • Applied Cross Validation for reliable model evaluation • Used GridSearchCV for hyperparameter tuning • Improved model performance and generalization Tools & Technologies: Python, pandas, NumPy, scikit-learn, matplotlib, seaborn This project helped me understand how to build more robust and reliable machine learning models by balancing bias and variance. Report attached below. #MachineLearning #DataScience #Python #AI #ModelTuning #LearningJourney

🚀 Project Launch: MNIST Image Classifier (Handwritten Digit Recognition) I’m excited to share my latest Machine Learning project — an MNIST Image Classifier that can accurately recognize handwritten digits from images. 🧠 What the model does: • Takes an image of a handwritten digit (0–9) • Processes and normalizes pixel data • Predicts the correct digit using a trained ML model 📊 Key Highlights: • Trained on the MNIST dataset • Built an end-to-end ML pipeline (data preprocessing → model training → evaluation) • Achieved high accuracy on handwritten digit recognition 💡 Tech Stack: Python | NumPy | Scikit-learn / TensorFlow | Computer Vision 🖥️ Application: Developed a simple and user-friendly interface to test predictions in real time. This project helped me strengthen my understanding of image classification, data preprocessing, and building practical ML systems. I’d love your feedback! 🙌 #MachineLearning #ComputerVision #AI #Python #DeepLearning #StudentProject

I’ve created simple and clear notes on Decision Tree, one of the most important algorithms in Machine Learning. 📌 In this, I covered: What Decision Tree is Tree structure (Root, Nodes, Leaves) How splitting works Gini Index, Entropy, Information Gain Step-by-step working Overfitting & Pruning Advantages & limitations Advanced concepts (Random Forest, Boosting) 🎯 This is useful for: Beginners in Data Science Students preparing for exams Anyone who wants strong fundamentals I explained everything in a simple and practical way with examples. 📄 Feel free to check it out and share your feedback! #MachineLearning #DecisionTree #DataScience #Python #AI #Learning #Beginners #DataAnalytics

Built a Machine Learning Project using #RandomForest + #GridSearchCV I developed a model to predict whether an esports player should be offered a contract based on performance and behavioral data. - Key Highlights: • Worked with both numerical and categorical features • Applied feature engineering to improve model learning • Used One-Hot Encoding for categorical data • Performed hyperparameter tuning using GridSearchCV • Achieved: Accuracy = 86 Precision = 89 Recall = 84 F1-score = 90 Key Learning: Improving data quality and feature relationships had a much bigger impact than just tuning the model. Once the data became more structured and meaningful, the model performance improved significantly. Tech Stack: Python | Pandas | NumPy | Scikit-learn | Random Forest | GridSearchCV - Grateful for the guidance from Abhishek Jivrakh Sir during this project. 🔗 Check out the project: [https://lnkd.in/g8qw8NMF] #MachineLearning #DataScience #AI #Python #RandomForest #GridSearchCV #Projects #LearningByDoing #Bagging #Boosting

Day 2 of Machine Learning Journey 🚀 Today, I continued working on Exploratory Data Analysis (EDA) — but this time with a completely different dataset. Key Realization 💡 : 70–80% of Machine Learning is actually EDA, Data Cleaning and Extraction, Feature Engineering and Selection. Every dataset teaches something new. I’m focusing on building strong fundamentals before jumping into models. you can check my work here, ( https://lnkd.in/gEEwAvT9 ) Goal is Consistency 🚀 #MachineLearning #EDA #DataScience #Python #LearningInPublic #AI #Consistency #LearningJourney