All Projects → ahmedbesbes → How-to-score-0.8134-in-Titanic-Kaggle-Challenge

ahmedbesbes / How-to-score-0.8134-in-Titanic-Kaggle-Challenge

Licence: other
Solution of the Titanic Kaggle competition

Programming Languages

Jupyter Notebook
11667 projects

Projects that are alternatives of or similar to How-to-score-0.8134-in-Titanic-Kaggle-Challenge

Machinelearningcourse
A collection of notebooks of my Machine Learning class written in python 3
Stars: ✭ 35 (-69.3%)
Mutual labels:  jupyter, scikit-learn, kaggle
Data-Science
Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-86.84%)
Mutual labels:  exploratory-data-analysis, kaggle
Cheatsheets.pdf
📚 Various cheatsheets in PDF
Stars: ✭ 159 (+39.47%)
Mutual labels:  jupyter, scikit-learn
dlime experiments
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (-81.58%)
Mutual labels:  random-forest, scikit-learn
Practical Machine Learning With Python
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
Stars: ✭ 1,868 (+1538.6%)
Mutual labels:  jupyter, scikit-learn
Py4chemoinformatics
Python for chemoinformatics
Stars: ✭ 140 (+22.81%)
Mutual labels:  jupyter, scikit-learn
ml-competition-template-titanic
Kaggle Titanic example
Stars: ✭ 51 (-55.26%)
Mutual labels:  kaggle, kaggle-titanic
Crime Analysis
Association Rule Mining from Spatial Data for Crime Analysis
Stars: ✭ 20 (-82.46%)
Mutual labels:  jupyter, scikit-learn
py4chemoinformatics
Python for chemoinformatics
Stars: ✭ 78 (-31.58%)
Mutual labels:  jupyter, scikit-learn
Bike-Sharing-Demand-Kaggle
Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand
Stars: ✭ 33 (-71.05%)
Mutual labels:  random-forest, kaggle
data-science-learning
📊 All of courses, assignments, exercises, mini-projects and books that I've done so far in the process of learning by myself Machine Learning and Data Science.
Stars: ✭ 32 (-71.93%)
Mutual labels:  scikit-learn, kaggle
Spark R Notebooks
R on Apache Spark (SparkR) tutorials for Big Data analysis and Machine Learning as IPython / Jupyter notebooks
Stars: ✭ 109 (-4.39%)
Mutual labels:  jupyter, exploratory-data-analysis
Computer Vision
Computer vision sabbatical study materials
Stars: ✭ 39 (-65.79%)
Mutual labels:  jupyter, scikit-learn
Ml Workspace
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Stars: ✭ 2,337 (+1950%)
Mutual labels:  jupyter, scikit-learn
handson-ml
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+150%)
Mutual labels:  random-forest, scikit-learn
Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn
Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…
Stars: ✭ 41 (-64.04%)
Mutual labels:  random-forest, scikit-learn
Lux
Python API for Intelligent Visual Data Discovery
Stars: ✭ 787 (+590.35%)
Mutual labels:  jupyter, exploratory-data-analysis
Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
Stars: ✭ 8,329 (+7206.14%)
Mutual labels:  jupyter, exploratory-data-analysis
kaggledatasets
Collection of Kaggle Datasets ready to use for Everyone (Looking for contributors)
Stars: ✭ 44 (-61.4%)
Mutual labels:  scikit-learn, kaggle
PracticalMachineLearning
A collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.
Stars: ✭ 60 (-47.37%)
Mutual labels:  scikit-learn, kaggle

How to score 0.8134 in Titanic Kaggle Challenge

The Titanic challenge on Kaggle is a competition in which the task is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. I have been playing with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0.8134 on the public leaderboard. As I'm writing this post, I am ranked among the top 9% of all Kagglers: More than 4540 teams are currently competing.

In a form of a jupyter notebook, my solution goes through the basic steps of a data science pipeline:

  • Exploratory data analysis with visualizations
  • Data cleaning
  • Feature engineering
  • Modeling
  • Modelfine-tuning

energy

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].