Titanic Survival Prediction - Machine Learning Model (76.54% Accuracy) -- 2

@EightVeer

Hi there, We can refine the Titanic survival model workflow by cleaning the dataset, validating the feature engineering around passenger Titles and Family_Size, and packaging the results in a reproducible Python notebook. We will also tighten the evaluation around the current 76.54% accuracy baseline with clear model comparison outputs and plots from Pandas, Scikit-Learn, Matplotlib, and Seaborn. One point to confirm is whether you want the final handoff as notebook only or notebook plus a short findings summary for the model, metrics, and feature impact. Best Regards, 8veer

@TheRSTeam

Hi, I am a data analyst/statistician with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@KolmanOuassou

Hi, I have been in the role for 6+ years and work as a data analyst, statistician and economist. I Have the ability to provide excellent work and the needs of your project. Would you be able to look at my profile and provide more information on previous projects and Reviews about my work as a contractor? Looking forward to your response. Best regards,

@muneeb6767

I have hands-on experience building Machine Learning solutions with Python, Pandas, NumPy, Scikit-Learn, and feature engineering, including a Titanic survival prediction model that achieved 76.54% accuracy through advanced preprocessing, title extraction, family-size engineering, and model optimization.

@arslanshahid386

Hi there, I see that you’re looking for someone to further develop a machine learning model for predicting Titanic passenger survival outcomes. With 4+ years of experience in data analysis and machine learning, I can help enhance your model's accuracy and functionality. My approach would involve thorough data preprocessing and cleaning, ensuring that any missing values are effectively addressed. I’d also focus on feature engineering to extract meaningful insights, like passenger titles and family size, which can significantly impact survival predictions. I have hands-on experience with Python, Pandas, and Scikit-Learn, and I am confident in training and evaluating various classification algorithms to optimize performance. What specific challenges have you faced in achieving the current accuracy of 76.54%, and are there particular areas you'd like to improve? Best regards, Arslan Shahid

@dinobartolome

Senior Full Stack Developer with 10+ years of experience — Python is part of my daily toolkit for scripting, automation, and data processing, and I've worked through ML pipelines similar to what you've built here. Your approach to the Titanic dataset shows solid methodology — using passenger titles for age imputation instead of just filling with the median is exactly the kind of domain-aware preprocessing that separates a serious analysis from a tutorial copy-paste. Same with engineering Family_Size as a derived feature rather than just feeding raw SibSp and Parch columns into the model. At 76.54% with Random Forest / XGBoost, there's a clear baseline to work from, and depending on what you need, there are concrete paths to push that higher — ensemble stacking, more aggressive hyperparameter tuning with GridSearchCV or Optuna, better cross-validation strategies, or additional feature engineering (cabin deck extraction, ticket prefix grouping, fare binning). I can help you identify which levers give the most lift for the least complexity. What you'd get working with me: I use AI heavily in my day-to-day workflow — Claude Code, custom agents, and AI-assisted development across every phase of work. For an ML project like this, that translates to faster iteration on feature engineering hypotheses, quicker model comparison cycles, and more thorough evaluation of preprocessing strategies. Instead of manually testing one approach at a time, I can spin up multiple experiments in parallel and converge on the best-performing pipeline faster. You get tighter turnaround without cutting corners on methodology. I also handle the full stack — if this needs to go beyond a Jupyter notebook into a deployed API, a simple web dashboard with Matplotlib or Seaborn visualizations baked in, or a clean portfolio-ready writeup with reproducible results, I can take it from notebook to production without you needing a second developer. Lower risk because there's no handoff gap. Clear milestones throughout — whether the goal is improving accuracy, adding new features to the model, building better visualizations, or packaging the whole thing for presentation, I'll break it into concrete deliverables so you see progress at every step instead of waiting for a final dump. Happy to jump on a quick 15-minute call to talk through exactly what you're looking to accomplish — whether that's pushing past 80% accuracy, deploying the model as a service, or extending the analysis with new features. Can start today.

@lalitdeveloper7

Hi, I have developed a Titanic Survival Prediction machine learning project using Python, Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. The project involved data cleaning, handling missing values with advanced age imputation based on passenger titles, feature engineering by creating attributes such as Title and Family Size, and training classification models including Random Forest and XGBoost. After evaluating multiple models, the solution achieved an accuracy of 76.54%, demonstrating strong skills in data preprocessing, feature engineering, model evaluation, and predictive analytics. Please let me know further. Thanks.

@imranahmadquresh

Hi, I understand you need Machine Learning Expert using Python to predict passenger survival outcomes based on historical maritime data from Titanic dataset. I offer my services for this project. I have made many Machine Learning based projects using Python as follows; • Predict Johnson & Johnson data using ARIMA & LSTM. • Stock Price Prediction of Amazon data & bank data using ARIMA & LSTM. • Handwritten Digit Recognition using Fourier response & SVM polynomial. • Classification of CIFAR-10 using different NN models. • Classification of Sentiment of Movie Review using Logistic Regression. • Classification of London Fire Brigade incidents 2019-2022 data using Decision Tree. • Vehicle Classification using PCA, k-NN, Logistic regression, Decision tree, random forest, NN & CNN. • IOT Attacks Prediction using SVM, Decision Tree & Random Forest. • Prediction extent of disease of ECG data using Random Forest & SVM. • Sign Language Recognition using SVM with normalization, standardization & data reduction. • Time Series Price Forecasting using Random Forest. • Classification of dementia disease using XGboost & Logistic Regression. • Classification of Muffin & Cupcake ingredient data using SVM. • Prediction of Solar Radiance using SVM & Bayesian Ridge. • Regression of Boston & Diabetes using Decision Tree, KNN & NN. • Regression of Wine quality using Random Forest. • Clustering fingerprint images using K-Means. I ensure to complete your project efficiently and on time.

@HNamdar

I can see you have built a solid foundation here with your Titanic survival model, including smart feature engineering like title extraction and family size. What you need now is someone to take it further and push accuracy beyond that 76.54% ceiling with tuned ensemble methods or refined hyperparameter optimization. I have 13 years of experience in data analysis and predictive modelling using Python, including scikit-learn, XGBoost, and feature selection pipelines. I have worked extensively on classification problems for academic and commercial research. I can review your current code, identify where the model is leaving performance on the table, implement improvements, and walk you through the results. Let me know what your specific goal is and I can get started right away.

@dataEntryExperty

Yes, please share the current v1 notebook. Reviewing your existing preprocessing pipeline will allow me to focus directly on improving model performance and implementing optimized feature engineering and model tuning for version 2. I have experience with Titanic survival prediction tasks using Scikit-Learn, Random Forest, XGBoost, and advanced feature engineering techniques, and I'll work to maximize accuracy while maintaining a robust and interpretable pipeline.

@Ritesh11gupta

I see you're looking for a model to predict Titanic survival. I've worked on similar projects using Python and data visualization tools to improve accuracy. What specific features are you interested in enhancing for this model?

@romany55

**DO NOT PAY ME UNTIL I COMPLETE! :)** Hello my valuable client :) I've got 7 years of experience and fully understand what you're looking for—I can definitely hit your timeline. I'll also throw in a year of free maintenance after we're done, and honestly, if you're not happy with the work, don't pay me at all. Just shoot me a message when you're ready and let's get started. I am eagerly waiting for your message. Roman

@southa5

i will supply you with 90% acuracy. With that said, my expertise in machine learning, Python and data analysis ensures I can build an effective model for your Titanic Survival Prediction project. I hold a PhD in computational science with a specialization in machine learning and deep learning. Specifically, I'm well-acquainted with the tools you mentioned: Pandas, NumPy, Scikit-Learn, Matplotlib, and Seaborn.

@rishi9343

I'll create a website design layout and review the design to ensure a user-friendly interface. I'll focus on website design, graphic design, and UI elements to deliver a clean and functional layout. With my understanding of web development and design principles, I'll provide a solid foundation for your website. Ready to start immediately — DM me.

@haseebsidd07

Hi there, I noticed your project on developing a Machine Learning model to predict passenger survival outcomes using the Titanic dataset. I am well-versed in the key responsibilities you've mentioned, such as data preprocessing, feature engineering, and model training. With over 6 years of experience in Python, Machine Learning, Data Mining, and Data Analysis, I specialize in transforming data into actionable insights. My skills in handling missing data and implementing advanced algorithms, such as Random Forest and XGBoost, will ensure we achieve and, if possible, exceed your desired accuracy of 76.54%. Please take a look at my portfolio showcasing relevant projects: https://www.freelancer.com/u/haseebsidd07 I am confident that my expertise can significantly contribute to your project’s success. Thank you for considering my proposal. Regards, Abdul Haseeb Siddiqui

@jaredp62

I’d be glad to help with this Titanic survival prediction project, especially the parts around preprocessing, feature engineering, and model comparison. The 76.54% accuracy shows a solid foundation, and I can build on that with clean, testable Python work that keeps the model interpretable and performance-focused. Jared

@artemb35

I understand you need a Machine Learning model to predict Titanic passenger survival with a target accuracy of 76.54%. I can deliver this by leveraging my expertise in data preprocessing, feature engineering, and model evaluation. Here’s my build plan: - **Data Preprocessing**: I'll handle missing data using advanced imputation techniques for ages based on passenger titles. - **Feature Engineering**: I will extract passenger titles and create a 'Family_Size' feature to enhance model accuracy. - **Model Training**: I will implement and compare classification algorithms like Random Forest and XGBoost to optimize for the required accuracy. - **Evaluation**: I'll use Scikit-Learn for model evaluation and Matplotlib/Seaborn for visualizing results. - **Documentation**: I will provide clear documentation of the process and results. I can start immediately and communicate directly throughout the project. Are there any specific datasets or additional features you would like me to focus on? Best, Artem

@msservic

Hello, I have strong experience in Machine Learning, Data Analysis, and Predictive Modeling using Python, Pandas, NumPy, and Scikit-Learn. Recently, I developed a Titanic Survival Prediction model with 76.54% accuracy using advanced feature engineering, data preprocessing, and classification algorithms. I also built a Heart Disease Prediction System using Logistic Regression and SVM models, leveraging 15 medical features to accurately predict heart disease risk. My expertise includes: • Data Cleaning & Preprocessing • Feature Engineering • Machine Learning Model Development • Statistical Analysis & Data Visualization • Python, Pandas, NumPy, Scikit-Learn, Matplotlib I can deliver a well-documented, accurate, and optimized solution within your budget and timeline. Looking forward to discussing the project.

@artur111shi

Will develop a machine learning model for Titanic survival prediction with 76.54% accuracy, utilizing data preprocessing, feature engineering, and model training. Implemented advanced imputation for missing ages using passenger titles and extracted meaningful features like 'Titles' and 'Family_Size'. Done in 2 days. What is the preferred format for the final model evaluation metrics?