titanic | Jupyter Notebook for Data Science Presentation | Machine Learning library

by markwest1972 HTML Version: Current License: MIT

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kandi X-RAY | titanic Summary

titanic is a HTML library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Jupyter applications. titanic has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Jupyter Notebook for Data Science Presentation. This Notebook shows examples of Feature Engineering and Hyperparameter tuning. The code is tested with Python 3.

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titanic has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

titanic has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of titanic is current.

Quality

titanic has no bugs reported.

Security

titanic has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

titanic is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

titanic releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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titanic Key Features

No Key Features are available at this moment for titanic.

titanic Examples and Code Snippets

No Code Snippets are available at this moment for titanic.

Community Discussions

Trending Discussions on titanic

What does read_csv() use random numbers for?

python dual for loops does not provide the expected results

What is the meaning of HIGH CORRELATION in pandas profiling?

PyTorch NN does not learn or learns poorly

How do you utilize array output from OneHotEncoder

Calculate the average budget of all movies in the data set

How to create stacked bar chart in python, color coded by category

How to plot each axes above the other with a for-loop

2-Way Anova Loop & Export to CSV or Excel Table

Distributions all numeric values in python

QUESTION

What does read_csv() use random numbers for?

Asked 2021-Jun-10 at 19:21

I just noticed that read_csv() somehow uses random numbers which is unexpected (at least to me). The corresponding base R function read.csv() does not do that. So, what does read_csv() use the random numbers for? I looked into the documentation but could not find a clear answer to that. Are the random numbers related to the guess_max argument?

...

ANSWER

Answered 2021-Jun-10 at 19:21

tl;dr somewhere deep in the guts of the cli package (called to generate the pretty-printed output about column types), the code is generating a random string to use as a label.

A major clue is that

Source https://stackoverflow.com/questions/67909394

QUESTION

python dual for loops does not provide the expected results

Asked 2021-Jun-06 at 22:20

I am new to python . i am trying to run the below code but the results are not as expected:

...

ANSWER

Answered 2021-Jun-06 at 21:17

There is no need for the nested loop.

Source https://stackoverflow.com/questions/67862765

QUESTION

What is the meaning of HIGH CORRELATION in pandas profiling?

Asked 2021-Jun-06 at 04:25

I'm trying to use pandas profiling on titanic dateset. Under the overview section there are some features with caption "HIGH CORRELATION"

I know what is the meaning of correlation, but the caption doesn't tell which feature is correlated to this feature ?
So what is the meaning of "HIGH CORRELATION" in the pandas profiling doc ?

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ANSWER

Answered 2021-Jun-06 at 04:25

If you click on the Warnings tab it will tell what other feature the features are correlated with as seen in this example. Can see the same thing in the example with the actual titanic data.

Source https://stackoverflow.com/questions/67855643

QUESTION

PyTorch NN does not learn or learns poorly

Asked 2021-Jun-04 at 17:03

I'm working with PyTorch tutorial, slightly modified to use Titanic dataset. I'm using very simple network of Linear(Dense) with ReLU... I'd like to predict survival status based on age, fare and sex for example.

I experienced a strange behavior with a simple neural network (I'm experimenting on Google Colab). Sometimes when I execute training, the accuracy doesn't change at all. It's strange because I'm recreating the model...

...

ANSWER

Answered 2021-Jun-04 at 17:03

As this is a classification problem, your neural network's last layer should not have a relu activation function.

Code Snippet:

Source https://stackoverflow.com/questions/67840919

QUESTION

How do you utilize array output from OneHotEncoder

Asked 2021-Jun-02 at 02:56

Python beginner here...

Trying to understand how to use OneHotEncoder from the sklearn.preprocessing library. I feel pretty confident in using it in combination with fit_transform so that the results can also be fit to the test dataframe. Where I get confused is what to do with the resulting encoded array. Do you then convert the ohe results back to a dataframe and append it to the existing train/test dataframe?

The ohe method seems a lot more cumbersome than the pd.get_dummies method, but from my understanding using ohe with fit_transform makes it easier to apply the same transformation to the test data.

Searched for hours and having a lot of trouble trying to find a good answer for this.

Example with the widely used Titanic dataset:

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ANSWER

Answered 2021-Jun-02 at 02:56

Your intuition is correct: pandas.get_dummies() is a lot easier to use, but the advantage of using OHE is that it will always apply the same transformation to unseen data. You can also export the instance using pickle or joblib and load it in other scripts.

There may be a way to directly reattach the encoded columns back to the original pandas.DataFrame. Personally, I go about it the long way. That is, I fit the encoder, transform the data, attach the output back to the DataFrame and drop the original column.

Source https://stackoverflow.com/questions/67797853

QUESTION

Calculate the average budget of all movies in the data set

Asked 2021-May-24 at 15:12

movies = [
     ("Titanic", 20000000),
     ("Dracula", 9000000),
     ("James Bond", 4500000),
     ("Pirates of the Caribbean: On Stranger Tides", 379000000),
     ("Avengers: Age of Ultron", 365000000),
     ("Avengers: Endgame", 356000000),
     ("Incredibles 2", 200000000)
 ]

...

ANSWER

Answered 2021-May-24 at 15:09

The normal approach for calculating averages would work here. Something along the lines of

Source https://stackoverflow.com/questions/67674347

QUESTION

How to create stacked bar chart in python, color coded by category

Asked 2021-May-16 at 19:23

I'm working on a popular Titanic dataset on Kaggle, and I would like to create a bar chart showing the numbers of survivors vs. deceased by gender. On the x-axis, I want gender (male/female). I want to have the survivors and deceased stacked and color coded.

Here is my current code, which produces four bars for each combination of male/survived, male/deceased, female/survived, female/deceased:

...

ANSWER

Answered 2021-May-16 at 16:29

With some example data I believe this is what you are looking for, using matplotlib:

Source https://stackoverflow.com/questions/67558783

QUESTION

How to plot each axes above the other with a for-loop

Asked 2021-May-16 at 18:55

I am trying to get visualizations from titanic dataset:

...

ANSWER

Answered 2021-May-16 at 18:55

You forgot to specify the axis for each plot, so it is plotting them all on the same axis.

Source https://stackoverflow.com/questions/67553058

QUESTION

2-Way Anova Loop & Export to CSV or Excel Table

Asked 2021-May-13 at 15:55

I've figured out how to run a 2-way anova on several variables in my data frame, but not sure how to get this into a format that could be easily exported to a csv file or excel. Ideally, I'd like it to have this in a format where each of my several hundred dependent variables is in it's own row, with the pVaules and Fvalues

I've made an example using the titanic dataset. In this case I've set Sex & Embarked as my categorical variables, and would like the output for the effects of Sex Embarked and ~Interaction somehow saved to a file. I'm open to suggestions on how to output this -- just want to be able to easily identify what values are significant, ideally with each dependent variable on its own line.

...

ANSWER

Answered 2021-Apr-09 at 04:34

You can extract the relevant statistics from the summary or store the model in a list and use broom::tidy on it to get all the stats together in a dataframe. Use map functions to run it on list of models.

Source https://stackoverflow.com/questions/67014880

QUESTION

Distributions all numeric values in python

Asked 2021-May-11 at 09:39

i am just doing titanic dataset machine learning problem.I seperate numerical and categorical value in my dataset.and want to plot histogram all numerical values but i doesnt show.Can anyone help me to fix this?My code:

...

ANSWER

Answered 2021-May-11 at 09:39

It seems that you just need to reset the plot with plt.figure() in each iteration:

Source https://stackoverflow.com/questions/67483369

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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Install titanic

You can download it from GitHub.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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