You will get Titanic Survival Prediction Model – From Raw Data to Insightful Results
Project details
This project involves building a machine learning model to predict survival outcomes of Titanic passengers based on structured data. I performed data cleaning, handled missing values, and selected relevant features. Categorical variables were encoded using LabelEncoder and OneHotEncoder, and numerical features were scaled for optimal model performance.
I experimented with different classifiers such as Logistic Regression and Random Forest, evaluating them using accuracy scores and cross-validation. The best-performing model was selected and integrated into a simple pipeline for easier predictions and reusability.
The project was built using Python with libraries such as pandas, scikit-learn, and seaborn for data handling, modeling, and visualization. Code is clean, modular, and well-commented to allow future improvements or integration.
This project demonstrates my ability to work with real-world datasets, apply machine learning techniques, and present results in a professional format. Deliverables include the full source code, sample predictions, and a brief summary of model performance. It’s a practical, production-ready project suitable for portfolios or educational referenc
I experimented with different classifiers such as Logistic Regression and Random Forest, evaluating them using accuracy scores and cross-validation. The best-performing model was selected and integrated into a simple pipeline for easier predictions and reusability.
The project was built using Python with libraries such as pandas, scikit-learn, and seaborn for data handling, modeling, and visualization. Code is clean, modular, and well-commented to allow future improvements or integration.
This project demonstrates my ability to work with real-world datasets, apply machine learning techniques, and present results in a professional format. Deliverables include the full source code, sample predictions, and a brief summary of model performance. It’s a practical, production-ready project suitable for portfolios or educational referenc
Machine Learning Tools
pandas, Python, Python Scikit-Learn, scikit-learnWhat's included
| Service Tiers |
Starter
$10
|
Standard
$25
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | |
Source Code |
About Mohammed
I Build Smart Solutions That Make Life Easier
Riyadh, Saudi Arabia - 4:25 pm local time
Steps for completing your project
After purchasing the project, send requirements so Mohammed can start the project.
Delivery time starts when Mohammed receives requirements from you.
Mohammed works on your project following the steps below.
Revisions may occur after the delivery date.
Data Cleaning and Feature Selection
Handle missing values, remove irrelevant columns, and select the most predictive features to prepare the dataset for model training.