GitHub - ayanand/GT_Assignment1
INSTALLED VERSIONS ------------------------------------------------------------------------------------------------------------------------- commit : b5958ee1999e9aead1938c0bba2b674378807b3d python : 3.7.12.final.0 python-bits : 64 OS : Linux OS-release : 5.4.104+ Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.5 numpy : 1.19.5 pytz : 2018.9 dateutil : 2.8.2 pip : 21.1.3 setuptools : 57.4.0 Cython : 0.29.24 pytest : 3.6.4 hypothesis : None sphinx : 1.8.5 blosc : None feather : 0.4.1 xlsxwriter : None lxml.etree : 4.2.6 html5lib : 1.0.1 pymysql : None psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 5.5.0 pandas_datareader: 0.9.0 bs4 : 4.6.3 bottleneck : 1.3.2 fsspec : None fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : 2.7.3 odfpy : None openpyxl : 2.5.9 pandas_gbq : 0.13.3 pyarrow : 3.0.0 pytables : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : 1.4.23 tables : 3.4.4 tabulate : 0.8.9 xarray : 0.18.2 xlrd : 1.1.0 xlwt : 1.3.0 numba : 0.51.2 Tensorflow : 2.6.0 ------------------------------------------------------------------------------------------------------------------------- Files Submitted - https://github.com/ayanand/GT_Assignment1/tree/main ------------------------------------------------------------------------------------------------------------------------- -- Fire_data.csv: Dataset used for Fire Incidence Data for city of Montreal -- Assignment_fire.ipynb : All the code run to do analysis of various models on Fire Incidence Data for city of Montreal in python notebook format -- assignment_fire.py : All the code run to do analysis of various models on Fire Incidence Data for city of Montreal in python format -- Assignment_Imdb.ipynb : All the code run to do this analysis of various models on Large Movie Review Dataset (Maas, 2011) in python notebook format -- assignment_imdb.py : All the code run to do this analysis of various models on Large Movie Review Dataset (Maas, 2011) in python format ------------------------------------------------------------------------------------------------------------------------- Citations/Bibliography ------------------------------------------------------------------------------------------------------------------------- Geron, A. (2019). Hands on Machine Learning with Scikit-Learn, Keras and Tensorflow. OReilly. Maas, A. L. (2011). Learning Word Vectors for Sentiment Analysis. Portland, Oregon, USA: Association for Computational Linguistics. https://github.com/ageron/handson-ml2 https://www.kaggle.com/arunmohan003/pruning-decision-trees-tutorial https://numpy.org/doc/stable/ https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html#:~:text=pyplot%20is%20a%20state%2Dbased,pyplot%20as%20plt%20x%20%3D%20np. https://pandas.pydata.org/docs/ https://docs.python.org/3/library/os.html https://docs.python.org/3/library/random.html https://docs.scipy.org/doc/scipy/reference/ https://seaborn.pydata.org/ https://docs.python.org/3/library/math.html https://www.tensorflow.org/api_docs https://docs.python.org/3/library/sys.html https://geopy.readthedocs.io/en/stable/ https://scikit-learn.org/stable/user_guide.html https://docs.python.org/3/library/glob.html https://ai.stanford.edu/~amaas/papers/wvSent_acl2011.bib https://radimrehurek.com/gensim/auto_examples/index.html https://www.kite.com/python/docs/nltk https://code.google.com/archive/p/word2vec/