This project builds an API that predicts Titanic survivors given the following columns Pclass
, Sex
, Age
, Number of Siblings/Spouse
, Number of Parents/Children
, Passenger Fare
, and Port of Embarkation
.
Model Building and EDA was executed in this colab notebook.
Exploratory Data Analysis was performed using Pandas-profiling
package. The Pandas-Profiling
package generates profile reports in html
or other extensions from a pandas DataFrame. The profile report can be found here. Download the report and open it in a browser to view the Exploratory Data Analysis.
Several Classifier algorithms were used on the training data, but the Random Forest Classifier Performed best on both accuracy
, f1
and ROC_AUC
metrics, hence its usage for training the entire cleaned dataset.
The project made use of the popular titanic dataset which can be gotten from here.
In order to execute this project on your local machine, you are expected create a python 3 virtual environment (see how here) and have pip installed in your environment. You are also expected to install all the necessary modules and libraries using pip in the requirements file.
To perform prediction:
- run the
api.py
script and click on the local host link - this takes you to a html page.
- You explore the data by viewing the first 10 rows, last 10 rows or get descriptive statistics of the dataset used for training the model, by clicking
Top view
,Bottom view
orShow dataset info
respectively. - Fill out the details of a passenger to perform prediction.
- Click on the
Predict
button to see if the passengerDied
orSurvived
the titanic. - Click on
Run Another Prediction
to perform another prediction.