لَآ إِلَـٰهَ إِلَّا هُوَ
LA ILAHA ILLA HU
Allah, Your Lord There Is No Deity Except Him.


هُوَ ٱلَّذِى يُسَيِّرُكُمْ فِى ٱلْبَرِّ وَٱلْبَحْرِ ۖ حَتَّىٰٓ إِذَا كُنتُمْ فِى ٱلْفُلْكِ وَجَرَيْنَ بِهِم بِرِيحٍۢ طَيِّبَةٍۢ وَفَرِحُوا۟ بِهَا جَآءَتْهَا رِيحٌ عَاصِفٌۭ وَجَآءَهُمُ ٱلْمَوْجُ مِن كُلِّ مَكَانٍۢ وَظَنُّوٓا۟ أَنَّهُمْ أُحِيطَ بِهِمْ ۙ دَعَوُا۟ ٱللَّهَ مُخْلِصِينَ لَهُ ٱلدِّينَ لَئِنْ أَنجَيْتَنَا مِنْ هَـٰذِهِۦ لَنَكُونَنَّ مِنَ ٱلشَّـٰكِرِينَ
He is the One Who enables you to travel through land and sea. And it so happens that you are on ships, sailing with a favourable wind, to the passengers’ delight. Suddenly, the ships are overcome by a gale wind and those on board are overwhelmed by waves from every side, and they assume they are doomed. They cry out to Allah ˹alone˺ in sincere devotion, “If You save us from this, we will certainly be grateful.”
فَلَمَّآ أَنجَىٰهُمْ إِذَا هُمْ يَبْغُونَ فِى ٱلْأَرْضِ بِغَيْرِ ٱلْحَقِّ ۗ يَـٰٓأَيُّهَا ٱلنَّاسُ إِنَّمَا بَغْيُكُمْ عَلَىٰٓ أَنفُسِكُم ۖ مَّتَـٰعَ ٱلْحَيَوٰةِ ٱلدُّنْيَا ۖ ثُمَّ إِلَيْنَا مَرْجِعُكُمْ فَنُنَبِّئُكُم بِمَا كُنتُمْ تَعْمَلُونَ
But as soon as He rescues them, they transgress in the land unjustly. O humanity! Your transgression is only against your own souls. ˹There is only˺ brief enjoyment in this worldly life, then to Us is your return, and then We will inform you of what you used to do.
(Al Quran Surah Yunus Ayas 22 & 23)
Titanic

Python Data Science Titanic Survival Prediction Using Machine Learning Classification Decision Trees Ensemble ML Random Forest Model Predicting the Survival of Titanic Using Titanic Dataset

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Titanic Survival Prediction Live Machine Learning Model At LFPPL.COM
How This Ensemble Random Forest Machine Learning Model Works?

Why Are We Using Ensemble Machine Learning Random Forest Classifier Machine Learning Model to Predict Titanic Disaster Survival Prediction Rate?

Ensemble Machine Learning Model: As we humans do seek opinions from other people in order for us to be able to take deisions that are free from biased or irrationalities. Ensemble machine learning model is one that makes predictions based on a number of different models. By combining individual models the ensemble model tends to be more flexible(less bias) and less data-sensitive(less variance).

Bagging and Boosting are two most popular Ensemble methods.

Bagging: In this method we train a bunch of individual models in a parallel way. Each model is trained by a random subset of the data.

Bagging-Method

Boosting: In this method we train a bunch of individual models in a sequential way. Each individual model learns from mistakes made by the previous model.


The Random Forest Classifier Machine Learning Model

The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by and large is more accurate than that of any individual tree.

Now let us see the steps involved in building this model.

First Load the required dataset.
Refer the Below Snapshot for loading the data.
Titanic Check if there are any missing values inside the dataset.
Titanic Get a brief description about the dataset.
Titanic Impute the numerical features by median value.
Titanic For categorical features, get the most occuring value(mode) for the feature and impute them.
Titanic Select the Dependent and Independent Features of the dataset.
We are not considering features that has more than 50 percent missing values.
Titanic After Feature Selection, perform preprocessing.
For binary categories, perform One Hot Encoding(OHE). Refer the snapshot below.
Titanic For more than 2 categories, Do Label Encoding.
Titanic For continious features, do min-max scaling.Refer the below Snapshot.
Titanic Titanic Count the number of Class Labels.
Titanic After Preprocessing, Run Classification models.
For Model Performance, We are considering Accuracy as well as confusion matrix.