Python Scipy Jobs

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Hourly - Intermediate ($$) - Est. Time: Less than 1 week, Less than 10 hrs/week - Posted
I am looking for a person who has experience performing statistical analysis using Python. I have a small dataset that consists of non-uniform time series data points (date of measurement, asset price) and I would like you to write a function that would take the dataset as input and produce an estimate of the present price for this asset as output. I would also like a brief explanation of the approach (i.e. do recent datapoints have more weight than older ones, are some outlying datapoints excluded, etc.). I would prefer you to utilize one of the existing Puthon stats analysis packages (pandas, numpy, statsmodels, scipy). Example dataset (a few more can be provided for validation/development): +------------+----------+ | date | price | +------------+----------+ | 2013-06-21 | 183000 | | 2013-10-04 | 178000 | | 2014-01-10 | 178500 | | 2013-11-22 | 176000 | | 2013-11-26 | 183000 | | 2014-02-01 | 184000 | | 2014-04-01 | 181500 | | 2015-05-30 | 228500 | | 2015-07-31 | 213000 | | 2015-08-13 | 232000 | | 2016-01-16 | 235000 | | 2015-11-22 | 210000 | | 2016-01-05 | 227500 | | 2015-12-16 | 220000 | | 2016-02-10 | 226000 | +------------+----------+
Skills: Python SciPy Python Numpy
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, 10-30 hrs/week - Posted
I have clean data that I have manipulated. Now, I need to just create tables based on regressions - this can be done either in STATA or in python. I have data in multiple formats - in panel data as well as separate files. You can use whichever is easier, but I need STATA or python to output one file for 26 regressions. I know how to run the regression on STATA, but I am not so familiar with it to know how to save the results and how to create charts.
Skills: Python SciPy Python Python Numpy Stata
Hourly - Expert ($$$) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
I have a data frame taken from an SQL database which needs to be transformed into a wide dataframe in Python using python 2.7. The dataframe will have many strings which need to be converted into a column with a 1 or 0. I would like this job completed by Monday 9am GMT as I have to build out the model and don't have time to build the model.
Skills: Python SciPy Python Numpy
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, Less than 10 hrs/week - Posted
Hi, I am looking who has patience to teach me Scrapy http://scrapy.org/ With Scrapy I want to crawl Amazon and some orther online shop to get the informations and prices, save then in MongoDB for later use Hope you can help me. Thank you Best wishes. Nguyen
Skills: Python SciPy Data Analytics Python Python Numpy
Hourly - Entry Level ($) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
Hi, I have an educational data set of how students performed on various learning tests. One set of students was in a condition with one version of a learning game (physics). The other set had a different version. Three schools were run on this experiment. I have attached the data set here. Students were measured based upon their: -Pre/post test scores/gain scores -Engagement survey results -Number of trials within the game itself -Actions used within the game itself -Trial times on incorrect trials within the game -Trial times on a mini-game within the main game (differed across two conditions) -Spatial ability -Attentional ability -A few other specific metrics I would like the following completed in sci-kit learn using Python: I. Exploratory statistics (scatterplots, histograms, etc.) II. Training and Testing of dataset: GridsearchCV Classifiers: Logistic Regression, Multinomial Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Classifier. Model evaluation metric: accuracy,precision,recall,f1-score,mean-squared error. III. Clustering (k-means, KNN) students performance on the final test based on: -Pretest scores (high vs. low) -Spatial ability (high vs. low) -Attentional ability (high vs. low) -Perhaps game performance metrics (actions used, trials, time spent) This should not be more than a days worth of work, possibly less. I realize some of these analyses may not make sense, we can discuss together to refine the strategy. I would like the output and code for all these analyses. Thanks!
Skills: Python SciPy Machine learning Python Python Numpy
Hourly - Intermediate ($$) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
We have a dataset of annotated Python functions, and we'd like to better understand how well we can predict the output type of a function given a natural language description of that function. Specifically, in this task we'd like to you build a classifier that, given a docstring, predicts whether a function returns a List. We have already applied dynamic analysis to these functions and will give you a dataset (see the attached file) that looks like: "split an integer into separate digits" True "reverse a list" True "add two numbers" False This dataset contains 259 positive examples and 341 negative examples. We'd like you to build a classifier using these data. We would like to know 1) what is the best accuracy and f1-score that can be achieved under cross-validation on this small dataset 2) what features (e.g. words or phrases) are most significantly associated with functions that return a List. Finally, an important secondary goal of this task is to evaluate Meta, a domain specific language for Python (http://www.meta-lang.org/tutorial). We require that you install and use Meta to instrument your code when completing this task. If you do well on this task, we have many other potential opportunities for work.
Skills: Python SciPy Data mining Machine learning Python