Python Numpy Jobs

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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 Numpy Machine learning Python Python SciPy
Fixed-Price - Intermediate ($$) - Est. Budget: $5 - Posted
Greetings, I need help creating an auto run file as described below:- There is an image attached for the root folder of a pen-drive, the requirement of the auto run file is to do the following:- when the pen drive is inserted in the computer to 1- check for frameworks and if it is not installed in the computer to run DotNtFx40_Full_x86_x64.exe and then to run the OpenEmrStart.exe. or 2- if frameworks is installed in the computer to run run the OpenEmrStart.exe. file. Thank you,
Skills: Python Numpy C# MySQL Programming
Hourly - Intermediate ($$) - Est. Time: Less than 1 month, Less than 10 hrs/week - Posted
We are working on an online fashion based application that provides personalized shopping experience to users based on their physical appearance. We are looking for someone in CV/IP field who might be interested to write initial algorithms for this project. The algorithm we need to write is to create 3D base templates/models for women's clothes. For example, we may need different base 3D models for different types of skirts. Similarly, we need to create templates/models for dresses, jeans, shoes and handbags. These models should support ability to size into different shapes/sizes e.g. XS, S, M, L and XL. We will be providing input pictures of the clothes (may be multiple images of the same clothes from different angles. no complex patterns). The algorithm is to scan the input pictures, identify the type of clothes, fit the base model using the corresponding clothes template and create texture maps. We will also need to change the base template to fit the scanned image and have the capability to create clothes in different sizes e.g. XS, S, L etc. Preferred language is python/c++/opencv/numpy. Please let us know if this is something you will be interested and we can plan on scheduling some time or chatting over the phone/skype for more questions. We would also need step-by-step approach and initial estimates in terms of time/price.
Skills: Python Numpy 3D Design 3D Modeling 3D Rendering
Hourly - Expert ($$$) - Est. Time: 1 to 3 months, 10-30 hrs/week - Posted
I'm a professor analyzing data of university student attributes and performance. I'm using it to try to predict/identify students who will likely have problems graduating. It's 10 years of data from a large university in the U.S. Lots of features. It should be very interesting. The tricky part is that I can't share the data with you, so we'd have to do some sort of live coding together. I've been teaching myself python, so I'd like to use that software. So I'd be using you as both a consultant for the modelling but also a mentor to help me become a better python programming. For the first part of this project, I'm envisioning about 10 hours of work together, though I'd like to start with just an hour or 2 to see how we work together. After that 10 hours, I would probably continue to hire you periodically for consulting. As I said, the data (which is already pretty clean) is 10 years of data which includes student attributes (GPA, demographics, SAT scores, performance in individual classes, graduation GPA, major...). The first part of the project would be data exploration and visualization. The second part would be statistical modeling. For instance, I'd like to model things like the probability of successfully graduating, the probability of switching majors, the probability of a significant decrease in GPA during the, say, next semester... What I'd like to do is have a preliminary meeting with the freelancer and try to work together for an hour or so, and if it works well then we can continue. I'm envisioning about 10 hours of work over the next month with the possibility of increasing that depending on my needs. The freelancer should have significant experience with python and scientific libraries. I'd prefer someone with strong statistical/machine learning skills too.
Skills: Python Numpy Python Python SciPy
Fixed Price Budget - Expert ($$$) - $5 to $19 - Posted
Note: this is not a coding task. *********************************** I've just take a simple pandas series, and plotted a bar chart using each of the following: pandas itself Seaborn vincent d3js mpld3 plot.ly Bokeh python-nvd3 Plot.ly is the only one that produced a chart that one can say was truly dynamic. And even then, the dynamism amounted to showing some values when the bars are moused over. And although some of the others provide the ability to zoom in, pan etc. and the technology is overall impressive and aesthetically pleasing, here's my issue: Charts generally live, get distributed in, and get displayed in, static media such as Word, PDF, and PowerPoint, in the vast majority of corporate and academic settings. That's not going to change any day soon. Yes there are cases where things like maps etc. lend themselves to the sort of D3-like capabilities of zooming in and seeing more granularity. But on the whole, I am not seeing the value of producing Scatter, Box, Line, Column and other charts in Python. Indeed, a suggestion I made recently to take some data and plot some of the above with Python was shot down, because the requester (a non-Python person), "would not be able to make revisions to the charts in Excel if she wanted". A very valid argument. As a Python developer, I want to be convinced that really taking the time and effort to dive into creating visualisations in Python, and mastering it, is going to be worth it professionally. The task is to provide me with some real examples (either from your own experience, or from industry) of where Python visualisations are actually preferred (not just "a pretty alternative", but actually *preferred*) over Excel. Examples from small, quirky, adventurous start-ups are not really what I'm interested in; we all know that these organisations are more adventurous and like the cutting edge of things. Nor am I interested in the examples of specialised, complex math based usage à la Matlab. I'm looking for examples of large-scale adoption in industry, or at least adoption *trends*. The deliverable is either a document, or links to some white papers, or some of your own portfolio, or whatever you think is best. You decide. Again, I'm not interested in seeing any actual code. How many is "some examples"? I'm not counting the amount of examples you provide. I'm interested in being convinced. Convince me. In your cover letter, just give me some indication that you've read the job spec and you understand what I'm after. I'm not looking to mine free advice - don't worry - so you can keep it short.
Skills: Python Numpy Big Data Data Analytics Data Science
Fixed-Price - Expert ($$$) - Est. Budget: $75 - Posted
Need assistance from accomplished numpy/scipy professional. Given two np-arrays A,B where A.shape=(n, m) dim(m)>=3 and B.shape=(nb,mb) dim(mb=6) create numpy sequence to determine average of i (1<i<6) shortest euclidean distances to B. Need speed of execution. We will provide test cases of A,B to validate solution.
Skills: Python Numpy Python SciPy
Fixed-Price - Intermediate ($$) - Est. Budget: $100 - Posted
I am analyzing data for academic publications (Multiple) and looking for a statistician who uses Pandas/Numpy/Scipy/Statmodels This job (the first of many) is finalizing the results section of a partially completed data analysis of a small data set. (n=77 with ~ 13 variables). Work includes multivariate model building, Correlations, generating graphs for publication. (initial descriptive and prelim multivariate is already done) Good communication skill required. Prior coaching experience would be a plus.
Skills: Python Numpy Python SciPy Statistics
Hourly - Entry Level ($) - Est. Time: Less than 1 week, 10-30 hrs/week - Posted
PROJECT INTRO This is a one time project, but I'm looking for someone I can work with long term on similar projects (usually ~5-15 hours/week). So please consider this project a trial to see if a longer term relationship makes sense. I plan on choosing several bidders and then working long term with one of them. The project is to go to Quantopian.com and translate one trading system idea I have into code (trading idea is detailed below). Quantopian is a free web app that allows you to input python scripts that create stock trading systems and then backtest them online using historical stock data (i.e., see how the system would have performed in the past). They provide the historical data. All you have to do is write the Python code. There is extensive documentation here: https://www.quantopian.com/help QUALIFICATIONS Experience with backtesting or trading isn't necessary, but the max hours to spend on this project is 6 hours. In other words, I'm willing to pay for some time getting familiar with using Quantopian and backtesting, but not for more education than that. So the ideal person someone familiar with backtesting and Python and can hit the ground running. A person familiar with Quantopian and Python should be able to do this in 1-2 hours at most, so if you can do it faster than 6 hours, great! All the more reason to work with you long term, since you already know what you're doing and you have proven you can work quickly! For this project, the simple trading system idea to turn into Python code in Quantopian is this: STRATEGY TO BUILD IN PYTHON - The one security to buy/sell is USO (oil stock in USA). - This will be traded as a weekly strategy, so you can think of it as a weekly bar chart chart of USO, meaning each bar on the chart is one week of data. ENTRY CONDITIONS - The rules for buying and selling: --- First, calculate the Average True Range indicator using the 9 weeks setting (ATR9). ---Each weekend, check if the ATR9 was up or down for the most recent week's completed bar vs. the ATR9 of 2 weeks ago's weekly bar. If the most recent ATR9 is up vs 2 week's ago, then enter a short position on USO. If down, then enter a long position of USO. - When you decide to enter a position on the weekend, enter at the opening on the next day (i.e., Monday open). --- There is one more entry condition: Calculate the 50 week moving average of USO, and only enter long positions when the the last bar's close > the 50 week moving average. Vice versa for short. EXIT CONDITIONS - Exit if ATR9 of 1 week vs 2 weeks ago ever reverses the entry conditions. I.e, for long, it is up 1 week ago vs 2 weeks ago, exit on the next Monday's open. - Alternatively, for exit, there is also a stop loss condition, which is set when you enter the position and it doesn't change until you close the position. That stop loss is calculated as 3 multiplied by the ATR9 of the bar you used when deciding to enter the position, added or subtracted to that same bar's closing price (depending on long or short). - Please note: The stop loss is checked daily each day at the end of the day, even though this is a weekly strategy. If the last day's high or low (daily bar) touched the stop loss number, then exit on the next day's open (doesn't matter what day of the week). OTHER STRATEGY DETAILS - Any capital not being used for USO for any reason should go into SHY instead. You can't short sell or otherwise trade SHY. It's used as a container for unused capital. - Whenever you enter a position, use 100% of capital available. - Use default settings for commission and slippage. FINAL DELIVERABLE Final deliverable is a copy/paste of the python code that you've already tested and ensured works bug free in Quantopian (just create your own free account and use the online IDE to test the code. If you hit "Build algorithm" and your code executes without bugs, then it works). HOURLY BILLING For this project, and long term, I'd like to bill hourly. That way if midway we talk and the rules change or run into complexity, you're fairly compensated for the extra work. Thanks for your bids!
  • Number of freelancers needed: 3
Skills: Python Numpy Algorithm Development MetaTrader 4 (MT4) Python
Hourly - Intermediate ($$) - Est. Time: 1 to 3 months, 10-30 hrs/week - Posted
Concept Overview: We're looking to expand our development team for quantitative & alogithmic trading developers. We're looking for more experts in Tradestation and ideally Python with experience in the quant finance space. Experience in Tradestation Optimization API, Python, Numpy, Scipy, Pandas, sckit-learn is required. FIX, Cuda, Numba experience a plus We're currently building a multi-strategy trading platform which will execute through Interactive Brokers or other FIX based brokers. Application Architecture Overview: The application (described in item #2 above) should take the complete TradeStation trading strategy source code as an input and optimize it using the TS 9.5 Optimization API. All source code for the project will be stored in a Git repository on GitHub. Developer Requirements: -- Good English Skills -- Ability to read software specifications (English) -- Can communicate with project manager on Skype text chat during the US Business -- -- Hours (6am – 3pm EST – New York City Timezone) -- Significant Experience in Tradestation, Python -- Experience using Git. -- Should be able to write clear, commented, well-structured code -- Must be detail oriented – no sloppy or lazy coding styles. -- Must be able to test their own code. Additional preferred skills of a developer: -- Understanding and experience with Software Development Lifecycle (SDLC) process -- Can provide ideas and feedback to improve overall software design. Our Company Overview Our company is a software application development company based in the United States. We focus on the financial and quantitative trading industries. We develop applications for both external customers and our own internal projects. We use Amazon Web Services (AWS) extensively. We are currently looking to expand our development team by adding freelance developers throughout the world. This project is an introductory project for new developers. If the developer does well on this project they will receive more work from us in the future. ** We will not respond to any phone calls about this job or work with any company outside of the oDesk system **
  • Number of freelancers needed: 2
Skills: Python Numpy Python