ML Kaggle Titanic Problem using LogisticRegrission

Overview

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission

here you will find the solution for the titanic problem on kaggle with comments and step by step coding



Problem Overview

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).


Table of Contents
  1. Analuze and visilaze the Dataset
  2. Clean and prepare the dataset for our ML model
  3. Build & Train Our Model
  4. Caluclate the Accuracy for the model
  5. Prepare the submission file to submit it to kaggle

Load & Analyze Our Dataset

  • First we read the data from the csv files
    data_train = pd.read_csv('titanic/train.csv')
    data_test = pd.read_csv('titanic/test.csv')

visilyze the given data

   print(data_train.head())
PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
0            1         0       3  ...   7.2500   NaN         S
1            2         1       1  ...  71.2833   C85         C
2            3         1       3  ...   7.9250   NaN         S
3            4         1       1  ...  53.1000  C123         S
4            5         0       3  ...   8.0500   NaN         S   

## Note ```sh The Survived column is what we’re trying to predict. We call this column the (target) and remaining columns are called (features) ```
### count the number of the Survived and the deaths ```py data_train['Survived'].value_counts() # (342 Survived) | (549 not survived) ```

plot the amount of the survived and the deaths

plt.figure(figsize=(5, 5))
plt.bar(list(data_train['Survived'].value_counts().keys()), (list(data_train['Survived'].value_counts())),
     color=['r', 'g'])

analyze the age

plt.figure(figsize=(5, 7))
plt.hist(data_train['Age'], color='Purple')
plt.title('Age Distribuation')
plt.xlabel('Age')
plt.show()


Note: Now after we made some analyze here and their, it's time to clean up our data If you take a look to the avalible columns we you may noticed that some columns are useless so they may affect on our model performance.

Here we make our cleaning function

   def clean(data):
    # here we drop the unwanted data
    data = data.drop(['Ticket', 'Cabin', 'Name'], axis=1)
    cols = ['SibSp', 'Parch', 'Fare', 'Age']

    # Fill the Null Values with the mean value
    for col in cols:
        data[col].fillna(data[col].mean(), inplace=True)

    # fill the Embarked null values with an unknown data
    data.Embarked.fillna('U', inplace=True)
    return data

# now we call our function and start cleaning!

data_train = clean(data_train)
data_test = clean(data_test)

## Note: now we need to change the sex feature into a numeric value like [1] for male and [0] female and also for the Embarked feature

Here we used preprocessing method in sklearn to do this job

le = preprocessing.LabelEncoder()
cols = ['Sex', 'Embarked'].predic
for col in cols:
    data_train[col] = le.fit_transform(data_train[col])
    data_test[col] = le.fit_transform(data_test[col])

## now our data is ready! it's time to build our model

we select the target column ['Survived'] to store it in [Y] and drop it from the original data

y = data_train['Survived']
x = data_train.drop('Survived', axis=1)

Here split our data

x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.02, random_state=10)

Init the model

model = LogisticRegression(random_state=0, max_iter=10000)

train our model

model.fit(x_train, y_train)
predictions = model.predict(x_val)

## Great !!! our model is now finished and ready to use

It's time to check the accuracy for our model

print('Accuracy=', accuracy_score(y_val, predictions))

Output:

Accuracy=0.97777

Now we submit our model to kaggle

test = pd.read_csv('titanic/test.csv')
df = pd.DataFrame({'PassengerId': test['PassengerId'].values, 'Survived': submit_pred})
df.to_csv('submit_this_file.csv', index=False)
Owner
Mahmoud Nasser Abdulhamed
Mahmoud Nasser Abdulhamed
Decision Tree Regression algorithm implemented on Python from scratch.

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

1 Dec 22, 2021
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022
Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

1 Jan 01, 2022
Turning images into '9-pan' palettes using KMeans clustering from sklearn.

img2palette Turning images into '9-pan' palettes using KMeans clustering from sklearn. Requirements We require: Pillow, for opening and processing ima

Samuel Vidovich 2 Jan 01, 2022
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
Anytime Learning At Macroscale

On Anytime Learning At Macroscale Learning from sequential data dumps (key) Requirements Python 3.7 Pytorch 1.9.0 Hydra 1.1.0 (pip install hydra-core

Meta Research 8 Mar 29, 2022
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 02, 2023
Sequence learning toolkit for Python

seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp

Lars 653 Dec 27, 2022
PennyLane is a cross-platform Python library for differentiable programming of quantum computers

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural ne

PennyLaneAI 1.6k Jan 01, 2023
Python package for machine learning for healthcare using a OMOP common data model

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

Sontag Lab 75 Jan 03, 2023
2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Fluid Simulation Usage Download this repo and store it in your computer. Open a terminal and go to the root directory of this folder. Make sure you ha

Mariana Ávalos Arce 5 Dec 02, 2022
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions

ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in

Computational Data Science Lab 182 Dec 31, 2022
vortex particles for simulating smoke in 2d

vortex-particles-method-2d vortex particles for simulating smoke in 2d -vortexparticles_s

12 Aug 23, 2022
PyTorch extensions for high performance and large scale training.

Description FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes. This library ext

Facebook Research 2k Dec 28, 2022
MasTrade is a trading bot in baselines3,pytorch,gym

mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in

Masoud Azizi 18 May 24, 2022
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

Igor Ivanov 671 Dec 25, 2022
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Generator of Rad Names from Decent Paper Acronyms

264 Nov 08, 2022
XManager: A framework for managing machine learning experiments 🧑‍🔬

XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction

DeepMind 620 Dec 27, 2022