Seeking freelancer experienced in Dynamic Time Warping using Python
Budget: $10 – $50 AUD
*Experience Freelancer in Dynamic Time Warping using Python*
I'm in need of a proficient freelancer with solid understanding and experience of Dynamic Time Warping (DTW) for time series analysis. The freelancer will be provided with a single .csv file containing valence ratings collected from multiple participants across different conditions. The goal is to compare real-time valence ratings with retrospective valence ratings at both the condition and individual participant levels.
Dataset:
• The dataset is in CSV format and contains emotion valence ratings.
• Valence ratings were collected from 24 participants under 6 different conditions
• Ratings values range between 1 and 27 and are sampled every 1000 ms (the duration of each condition varies between 1 - 2 minutes)
Requirements:
1. DTW Analysis: Conduct DTW analysis at two levels:
○ Condition Level: Group participants into 6 conditions and perform DTW analysis for each condition.
○ Participant Level: Group participants by their participant number and conduct DTW analysis for each participant.
2. Programming Language and Libraries:
• Utilize Python for the analysis.
• Implement DTW using relevant Python libraries (e.g., fastdtw, dtw, or others).
• Clearly state the libraries used and provide any necessary installation instructions.
3. Jupyter Notebook:
• Deliver the code in a Jupyter Notebook format (.ipynb).
• Include detailed comments and markdown cells for clarity.
• Structure the notebook logically with sections for data loading, preprocessing, analysis, and interpretation; include data visualisations where needed
4. Data Input:
• You will be provided with a single CSV file as the input data source.
5. Output:
• Include python code for loading and preprocessing the data into
• Provide clear and concise interpretation of the DTW analysis results.
• Explain any patterns or insights (including visualisations) obtained from the comparison of real-time and retrospective valence ratings.
Deliverables:
• Submit the Jupyter Notebook (.ipynb) along with any additional files needed for execution.
Include a brief summary of your findings
Key Skills and Experience:
- Proficiency in Python programming
- Prior experience with time series analysis
- Familiarity with Dynamic Time Warping
- The ability to handle any type of time series data
- Experience with time series visualisation in python
- Flexibility and problem-solving skills
This project must be completed within 24 hours. This is a simple project and should only take a a couple of hours to complete if you are a a freelancer who is experienced in Dynamic Time Warping using python. The total estimated budget for this project is $50 AUD. If you are interested in this project, please provide your bid along with a description of (a) your experience in DTW analysis in Python; and (b) the Python libraries you intend to use for the analysis and why. Freelancers not providing this information will not be considered.
I'm in need of a proficient freelancer with solid understanding and experience of Dynamic Time Warping (DTW) for time series analysis. The freelancer will be provided with a single .csv file containing valence ratings collected from multiple participants across different conditions. The goal is to compare real-time valence ratings with retrospective valence ratings at both the condition and individual participant levels.
Dataset:
• The dataset is in CSV format and contains emotion valence ratings.
• Valence ratings were collected from 24 participants under 6 different conditions
• Ratings values range between 1 and 27 and are sampled every 1000 ms (the duration of each condition varies between 1 - 2 minutes)
Requirements:
1. DTW Analysis: Conduct DTW analysis at two levels:
○ Condition Level: Group participants into 6 conditions and perform DTW analysis for each condition.
○ Participant Level: Group participants by their participant number and conduct DTW analysis for each participant.
2. Programming Language and Libraries:
• Utilize Python for the analysis.
• Implement DTW using relevant Python libraries (e.g., fastdtw, dtw, or others).
• Clearly state the libraries used and provide any necessary installation instructions.
3. Jupyter Notebook:
• Deliver the code in a Jupyter Notebook format (.ipynb).
• Include detailed comments and markdown cells for clarity.
• Structure the notebook logically with sections for data loading, preprocessing, analysis, and interpretation; include data visualisations where needed
4. Data Input:
• You will be provided with a single CSV file as the input data source.
5. Output:
• Include python code for loading and preprocessing the data into
• Provide clear and concise interpretation of the DTW analysis results.
• Explain any patterns or insights (including visualisations) obtained from the comparison of real-time and retrospective valence ratings.
Deliverables:
• Submit the Jupyter Notebook (.ipynb) along with any additional files needed for execution.
Include a brief summary of your findings
Key Skills and Experience:
- Proficiency in Python programming
- Prior experience with time series analysis
- Familiarity with Dynamic Time Warping
- The ability to handle any type of time series data
- Experience with time series visualisation in python
- Flexibility and problem-solving skills
This project must be completed within 24 hours. This is a simple project and should only take a a couple of hours to complete if you are a a freelancer who is experienced in Dynamic Time Warping using python. The total estimated budget for this project is $50 AUD. If you are interested in this project, please provide your bid along with a description of (a) your experience in DTW analysis in Python; and (b) the Python libraries you intend to use for the analysis and why. Freelancers not providing this information will not be considered.
Related categories:
Python
Statistics
Machine Learning (ML)
Statistical Analysis
Time Series Analysis