#DataScience Project 1
Titanic – Machine Learning from Disaster
Use Machine Learning to create a model that predicts which passengers survived the Titanic shipwreck.
Libraries Used
- Numpy
- Pandas
- Seaborn
- Sickit-Learn
Final Model Chosen
- Decision Tree: 93.03% accuracy🔥
Titanic – Machine Learning from Disaster
Use Machine Learning to create a model that predicts which passengers survived the Titanic shipwreck.
Libraries Used
- Numpy
- Pandas
- Seaborn
- Sickit-Learn
Final Model Chosen
- Decision Tree: 93.03% accuracy🔥
The data science methodology followed has been outlined by John Rollins, IBM
- Business Understanding
- Analytical Approach
- Data requirements
- Data collection
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
Project Code 👇
github.com/Piyal-Banik/Ti…
- Business Understanding
- Analytical Approach
- Data requirements
- Data collection
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
Project Code 👇
github.com/Piyal-Banik/Ti…
1. Business Understanding
Given a passenger's information, how can we predict whether he/she survived the Titanic disaster?
2. Analytical Approach:
Our target variable is categorical [survived / not survived], and hence we need classification models for this task.
Given a passenger's information, how can we predict whether he/she survived the Titanic disaster?
2. Analytical Approach:
Our target variable is categorical [survived / not survived], and hence we need classification models for this task.
3, 4. Data Requirements & Data Collection:
[Combined these two steps together as the datasets are given on Kaggle]
We are given 2 datasets, one for training our model and the other to test if our model can determine survival based on observations, not having the survival info.
[Combined these two steps together as the datasets are given on Kaggle]
We are given 2 datasets, one for training our model and the other to test if our model can determine survival based on observations, not having the survival info.
5. Data Understanding
This step is part of Exploratory Data Analysis
The shape of the datasets
- Training set (891,12)
- Test set (418,11)
In total there are 12 features in the training set and 11 features in the test set 👇
This step is part of Exploratory Data Analysis
The shape of the datasets
- Training set (891,12)
- Test set (418,11)
In total there are 12 features in the training set and 11 features in the test set 👇
Feature types
- Continous: Age, Fare
- Discrete: SibSp, Parch
- Categorical: Survived, Sex, and Embarked
- Ordinal: Pclass
- Mixed: Ticket
- Alphanumeric: Cabin
Features with missing values
- Cabin
- Age
- Embarked
- Continous: Age, Fare
- Discrete: SibSp, Parch
- Categorical: Survived, Sex, and Embarked
- Ordinal: Pclass
- Mixed: Ticket
- Alphanumeric: Cabin
Features with missing values
- Cabin
- Age
- Embarked
Statistical Information of the training dataset
Finding out the relationship of predictor variables with the target variables:
- Pclass = 1 more likely to survive
- Sex = Female more likely to survive
- most of age = 15-25 did not survive
- high fare had better survival
- Port of embarkation correlates with survival rates
- Pclass = 1 more likely to survive
- Sex = Female more likely to survive
- most of age = 15-25 did not survive
- high fare had better survival
- Port of embarkation correlates with survival rates
6. Data Preparation
Cleaning steps based on analysis:
- Impute the missing Age values
- Turn age into an ordinal feature
- Impute missing Embarked values
- drop Cabin [too many missing values]
- drop Ticket [many duplicates]
- drop PassengerID, Name, SibSp, Parch [not helpful]
Cleaning steps based on analysis:
- Impute the missing Age values
- Turn age into an ordinal feature
- Impute missing Embarked values
- drop Cabin [too many missing values]
- drop Ticket [many duplicates]
- drop PassengerID, Name, SibSp, Parch [not helpful]
Feature Engineering Steps
Created Dummy Variables for
- Sex
- Embarked
Created Dummy Variables for
- Sex
- Embarked
7. Modeling
We are ready to train our model and predict the output.
Models trained
- Logistic Regression
- k-Nearest Neighbors
- Support Vector Machines
- Naive Bayes classifier
- Decision Tree
- Random Forest
We are ready to train our model and predict the output.
Models trained
- Logistic Regression
- k-Nearest Neighbors
- Support Vector Machines
- Naive Bayes classifier
- Decision Tree
- Random Forest
8. Evaluation
Decision Tree and Random Forest achieved the maximum accuracy of 93.03%. We can choose anyone as a final model.
Decision Tree and Random Forest achieved the maximum accuracy of 93.03%. We can choose anyone as a final model.
That's it for this tread 👋
Please do point out if you feel I have done some mistakes!
A retweet for the first one would really mean a lot 🙏
If you liked my content and want to get more threads on Data Science, Machine Learning & Python, do follow me @PiyalBanik
Please do point out if you feel I have done some mistakes!
A retweet for the first one would really mean a lot 🙏
If you liked my content and want to get more threads on Data Science, Machine Learning & Python, do follow me @PiyalBanik
Here is the ladder 🪜 to the top
https://twitter.com/PiyalBanik/status/1419239949136584704?s=20
Two mistakes
- scikit-learn spelling
- should not have mentioned the training accuracy, it's misleading. Test accuracy was 76.55
- scikit-learn spelling
- should not have mentioned the training accuracy, it's misleading. Test accuracy was 76.55
https://twitter.com/PiyalBanik/status/1419239949136584704?s=20
• • •
Missing some Tweet in this thread? You can try to
force a refresh
