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I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook.…

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Nyc-taxi-Kaggle-challenge

Goal

Kaggle competition to predict NYC taxi travel times. The report for the project is at capstone.pdf. (In this project i provide many links through which clear your concept if you are beginner , if anything not understand go throughly link and pdf which provides in project and readme .)

PROBLEM S TATEMENT

In this report, we look at a Kaggle competition with data from the NYC Taxi and Limousine Commission, which asks competitors to predict the total ride time (trip_duration) of taxi trips in New York City. The data provided by Kaggle is structured data provided as a CSV file. The data in the CSV file includes multiple formats: timestamps, text, and numerical data. This is a regression analysis, since the output, total ride time, is numerical. I will use several machine learning methods for the prediction task, which are linear regression, k-nearest neighbors regression, random forests, and XGBoost. The models will be evaluated using the root mean squared logarithmic error.

Overview

i perform this project on dekstop with using Jupyter_Notebook and also perform without using Jupyter_notebook on remote server using python.

Software and Libraries

  • Python 3
  • Scikit-learn: Python’s open source machine learning library
  • XGBoost: Python package for XGBoost model,

Datasets

The primary train dataset (train.csv) and test dataset (test.csv) is at the Kaggle competition website.

The weather dataset is at: weather_data_nyc_centralpark_2016.csv.

The datasets for the fastest routes from OSRM can be found here. The files are: fastest_routes_train_part_1.csv, fastest_routes_train_part_2.csv, and fastest_routes_test.csv

Visualization Image

The final visualization image for the project report is visualization.pdf (.png), and is best viewed zoomed in. h

Credits

Credits for this code go to rebeccak1

Credits for this code go to:

[https://github.com/llSourcell/kaggle_challenge/blob/master/notebook.ipynb]

[https://www.youtube.com/watch?v=suRd3UzdBeo]

Remote Server Configuration:

Learn copy transfer file from host to remote server and vice versa [http://qr.ae/TUNL43].

More Learning Resources:

install the library : pip install graphviz

https://www.kaggle.com/kanncaa1/machi...

https://www.kaggle.com/rtatman/beginn...

https://machinelearningmastery.com/ge...

http://blog.kaggle.com/2017/01/23/a-k...

https://www.youtube.com/watch?v=suRd3UzdBeo....

This video is apart of my Machine Learning Journey course:

https://github.com/llSourcell/Machine....

basic resources

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html .....(df.corr)

https://study.com/academy/lesson/pearson-correlation-coefficient-formula-example-significance.html

https://seaborn.pydata.org/generated/seaborn.diverging_palette.html ...........(cmap = sns.diverging_palette )

https://stackoverflow.com/a/34667386/5879056 ....(np.where )

https://www.geeksforgeeks.org/python-format-function/ (string.format)

http://qr.ae/TUNfyZ

http://qr.ae/TUNfrF

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html .........(df.loc)....->>Access a group of rows and columns by label(s) or a boolean array.

https://www.tutorialspoint.com/numpy/numpy_introduction.htm...

https://www.tutorialspoint.com/seaborn/seaborn_quick_guide.htm......

https://www.tutorialspoint.com/python_pandas/python_pandas_introduction.htm...

https://www.datacamp.com/community/tutorials/xgboost-in-python.... ## Necessarily read (Xgboost)

https://stackoverflow.com/questions/43027980/purpose-of-matplotlib-inline/43028034 //Provided you are running IPython, the

%matplotlib inline will make your plot outputs appear and be stored within the notebook.

/Scikit-learn

https://www.datacamp.com/community/tutorials/machine-learning-python......

https://www.analyticsvidhya.com/blog/2015/01/scikit-learn-python-machine-learning-tool/....

scikit-learn is used to build models. It should not be used for reading the data, manipulating and summarizing it. There are

better libraries for that (e.g. NumPy, Pandas etc.)

http://scikit-learn.org/stable/tutorial/basic/tutorial.html.....

https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/

https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/...

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html...

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html...

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html....

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html...

remove a directory through terminal contain with many files

"rm -rf ankit"

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I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook.…

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