I need you work on this carefully like a student learning.
I need you work on this carefully like a student learning. And the background of screenshot have to be white
Introduction
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, you will build a predictive model (as a supervised learning classifier) that answers the question: “what sorts of people were more likely to survive?” using passenger data (i.e., name, age, gender, socioeconomic class, etc.).
Your ultimate goal is to build a model that has a predictive (test) accuracy higher than those of your classmates. This is a competition.
Data
The raw data is provided in the following CSV file, which contains 1309 rows and 14 columns. The target/label column is ‘survived’. All others are potentially useful features.
titanic_full.csv Download titanic_full.csv
Tasks
Submit a completed Jupyter notebook file containing the following steps and related output:
Import all necessary libraries and modules.
Load the dataset into a Pandas DataFrame.
Perform exploratory data analysis (EDA):
Explore and understand the dataset as a whole.
Explore and probe the many rows and columns. Use visualizations, incl. histograms and any other appropriate plots.
Identify and remedy any deficits (e.g., missing, null data) that need to be addressed in order to do any meaningful analysis.
Perform feature analysis, engineering and selection:
Apply encoding as necessary to make the data usable in training a machine learning model.
Apply scaling/normalization/standardization as appropriate. (may be repeated in multiple, iterative experiments)
Perform basic feature correlation analysis (your choice of approach–e.g., Pearson corr. coef. matrix)
If you find any features that appear to yield little or no predictive value, consider discarding them.
Be cautious, though, as simple linear correlation may not reveal the links among features and target.
Perform any other feature selection and elimination methods you feel are appropriate. Must include at least ONE (1) other than feature correlation from 4.3.
If you feel it’s appropriate, apply PCA. I would suggest that you wait until training and testing a model before using PCA and then re-training/testing.
Split your data into training and test sets.
Choose a supervised learning algorithm and use it to train a model to predict whether or not a passenger survived based on the features you’ve selected/engineered.
Calculate and output your training and testing accuracies as well as confusion matrices for training and testing. Improve your model by modifying hyperparameters, if possible (e.g., k in knn).
You may optionally use cross-fold validation for testing and iterative training.
Repeat as many of the steps above as needed to build a more accurate (i.e., testing accuracy) model–varying your application and choice of feature engineering methods and ML algorithms. Continue repeating the process until you’re satisfied.
The winner gets a 2 point course grade bonus. Second place gets 1 point. Third gets 0.5 points.
Remember, your model must demonstrate a high TESTING accuracy, not just training accuracy. Only testing performance (measured by testing accuracy, F1, recall, sensitivity, etc.) can reveal the predictive power–generalizability–of the model.
Submission:
Notebook file (or link to Colab notebook)
Word document with:
at least 3 screenshots, including the training and testing outcomes
a short write-up of your approach and steps taken–make sure to note your final test accuracy scores.
Collepals.com Plagiarism Free Papers
Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.
Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS
Why Hire Collepals.com writers to do your paper?
Quality- We are experienced and have access to ample research materials.
We write plagiarism Free Content
Confidential- We never share or sell your personal information to third parties.
Support-Chat with us today! We are always waiting to answer all your questions.
